Vegan Gains presented a stat stupidly and poorly and pretty much died on that hill.
Destiny brought in a dude who "knows" stats but he just kinda, well, did not know stats...
The stat vegan gains brought forward presented the all cause mortality rate and concluded that they were fairly similar. As a dude who's taken both mathematical statistics and econometry i can personally say that this is probably correct. -HOWEVER- both vegan gains AND the guy that destiny brings in at 32:50 are incorrect about what the "confidence intervals" are. Firstly, the models in the study are, as stated in the study, Cox Proportional-Hazards model's. Essentially these are regression model's comparing relationtips, in this case how several different regressors affect survival. Basically, a hazard ratio >1 means and increase risk of death, =1 no increase/decrease and <1 a decrease. Secondly, the stats and the 95% confidence intervals themselves. So basically, in this study, vegan gains is correct that the confidence intervals for vegetarians and meat eaters are tighter than the other groups. Generally, a tighter confidence interval is "good" since it generally means a more accurate result. The stat guy that destiny brough in SHOULD HAVE noticed this because it essentially means, in the context of the debate, that we can be 95% certain that eating vegetarian/vegan does reduce all-cause mortality. The other diets had a span that reached over 1 which means that we can't be 95% sure that it doesn't decrease all-cause mortality. We could use a lower confidence interval, maybe like 90% for the other diets and we could expect the interval to be below 1. This stat dude that destiny brought just doesn't understand what he's talking about. The hazard ratio confidence intervals being between 0.84-0.99 means that we can be 95% certain that there is NO increase in all-cause mortality. Whilst if the confidence interval was between 0.85-1.02 then that basically means that it could be a decrease OR an increase, with a decrease being more likely. Vegan Gains points seem to be that with 95% confidence a vegetarian/vegan diet DOES decrease all-cause mortality. At 37:00 actually says this. The dude destiny brought in responds to this by saying "i think you're confound confidence intervals and test statistics". Whilst it is true that only a confidence interval literally says nothing, but the stats dude that destiny brought in doesn't realize that the HR values is the result of the regression model. In this context the confidence intervals actually tells us the range of the tests results, i.e. the confidence intervals is not "just" a confidence interval. In conclusion, Vegan Gains is correct but doesn't realize that the margins are so slim that it isn't significant so it doesn't really matter. The study concludes that the all-cause mortality rate is similar, which it is because the difference is so marginal that to say otherwise would be weird. Especially since the study even admits that " The study participants are not representative of the United Kingdom population, but the mean intake of red meat in our reference group of regular meat eaters are similar to those of adults aged 19–64 y in the UK National Diet and Nutrition Survey." So it would be strange to draw specific conclusions.
[Q] Survival analysis + cox regression: How to estimate the effect of a covariate vs. no baseline hazard in cox regression?
This is a rather nuanced question but hopefully an expert out there can help me! I'm running a survival analysis with standard cox regression on multiple covariates. Let's say between conditions A and B they have different lifespans (survival times). I include covariates 1, 2, and 3 in the model. I can compare the contributions of each covariate using the log-likelihood ratio, and covariate 2 has a large contribution to the log likelihood and is very significant. What I want to know is whether the explanatory power of the covariate is larger than the baseline contribution of time to event itself. That is, consider scenario A: -Hazard rate is actually constant and independent of time. It's just 2% failure rate at every interval, and the covariate increases this to 4%. Then I would say time itself has *no* explanatory power, as the hazard is constant. If this were a typical linear regression, the R^2 for including time would be 0. Then including a covariate might make the model much more accurate. -Scenario B: -Hazard rate increases exponentially with time, so that even though covariate A may increase hazard proportionally, in a 60 years old individual their hazard is 10%, vs 20% for those with the covariate. But at age 20, the hazard might be .00001% vs. .00002%. In this scenario, clearly time is the major contributing factor overall to hazard, even though the covariate may be highly significant. So my big picture question between the two scenarios is how much time itself contributes to the accuracy of the model vs. the added covariates. I can't figure out how to statistically compare these two scenarios. I guess I basically want an anova on the cox models where the null model is not the baseline hazard, but rather a constant hazard? And I use the constant model as the baseline log likelihood? Does anyone have any idea what to do here to make this proper and rigorous?
Sorry if this a super basic question, but I have the following question as part of a uni assignment and I'm very new to statistics: What is the survival time following infection with C0VID-19 and are there differences according to patient sex or age? The data set provided contains date of diagnosis, patient status (dead/not dead), date of death, sex, age, city, province, country. My question is, will survival analysis using Kaplan-Meier survival curves and log-rank tests be sufficient to answer this question? Or should I analyse covariates and report hazard ratios using the Cox PH model?
Background: High density lipoprotein cholesterol concentration (HDL-C) is an established atheroprotective marker, in particular for coronary artery disease; however, HDL particle concentration (HDL-P) may better predict risk. The associations of HDL-C and HDL-P with ischemic stroke and with myocardial infarction (MI) among women and Blacks has not been well studied. We hypothesized that HDL-P would be consistently associated with MI and stroke among women and Blacks compared with HDL-C. Methods: We analyzed individual level participant data in a pooled cohort of four large population studies without baseline atherosclerotic cardiovascular disease (ASCVD) - the Dallas Heart Study (DHS) (n=2,535), Atherosclerotic Risk in Communities (ARIC) Study (n=1,595), Multi Ethnic Study of Atherosclerosis (MESA) (n=6,632) and Prevention of Renal and Vascular Endstage Disease (PREVEND) (n=5,022). HDL markers were analyzed in adjusted Cox proportional hazard models for MI and ischemic stroke. Results: In the overall population (n=15,784), HDL-P was inversely associated with the combined outcome of MI and ischemic stroke, adjusted for cardiometabolic risk factors, [hazard ratio (HR) for Q4 vs Q1 0.64, 95% confidence interval [CI] 0.52 to 0.78] as was HDL-C (HR for Q4 vs Q1: 0.76, 95% CI 0.61 to 0.94). Adjustment for HDL-C did not attenuate the inverse relationship between HDL-P and ASCVD, while adjustment for HDL-P attenuated all associations between HDL-C and events. HDL-P was inversely associated with the individual endpoints of MI and ischemic stroke in the overall population, including in women. HDL-P was inversely associated with MI among White participants but not among Black participants (HR Q4 vs Q1 for White 0.49, 95%CI 0.35-0.69; for Black 1.22, 95%CI 0.76-1.98; pinteraction = 0.001). Similarly, HDL-C was inversely associated with MI among White participants (HR Q4 vs Q1 0.53, 95%CI 0.36-0.78) but had a weak direct association with MI among Black participants (HR Q4 vs Q1 1.75, 95%CI 1.08-2.83; pinteraction < 0.0001). Conclusions: In comparison to HDL-C, HDL-P was consistently associated with MI and ischemic stroke in the overall population. Differential associations of both HDL-C and HDL-P for MI by Black ethnicity suggest that ASCVD risk may differ by vascular domain and ethnicity. Future studies should examine individual outcomes separately. Full pdf: https://www.ahajournals.org/doi/reade10.1161/CIRCULATIONAHA.120.045713 Clinical Perspective What is new?
HDL-P is inversely associated with the specific endpoint of ischemic stroke overall and among women, whereas HDL-C is not associated with ischemic stroke.
Neither HDL-P nor HDL-C are associated with myocardial infarction in Blacks.
What are the clinical implications?
HDL-P but not HDL-C may be a useful risk marker for ischemic stroke.
HDL-P may be a useful risk marker for both myocardial infarction and ischemic stroke among women.
There is likely minimal utility of HDL markers for risk prediction of myocardial infarction in the Black population.
Questions regarding survival analysis (Cox regression and log rank test to be specific)
Ok so I have this dataset where I’m comparing Monotherapy vs combination therapy in terms of overall survival (OS) and hazard ratio for a specific disease. Now I ran a log rank test on the OS, and it turned out statistically significant that there is a difference between the both survival curves (mono vs combo). So my questions are as follows: 1)Should I proceed to univariate cox regression? Like let’s say there was no statistical significance in OS using log rank test, should I still do univariate cox regression? 2)The covariate in case of univariate cox regression would be whether the patient received monotherapy or combination therapy, right? 3)Let’s say I had 5 variables and 2 of them are not statistically significant, should I still include them in my multivariate cox regression? 4)If my univariate result was statistically significant but not statistically significant in multivariate regression, what should I exactly report/interpret? I’m fairly new to this and I can’t really find a good resource, so if anyone could direct me towards a great resource to understand cox regression specifically I would be forever thankful.
Results from the first long-term cohort study of more than 36,000 Japanese men over decades suggest an association between eating mushrooms and a lower risk of prostate cancer. "To the best of our knowledge, this is the first cohort study indicating the prostate cancer-preventive potential of mushrooms at a population level," said Zhang. "Although our study suggests regular consumption of mushrooms may reduce the risk of prostate cancer, we also want to emphasize that eating a healthy and balanced diet is much more important than filling your shopping basket with mushrooms." said Zhang. Long-term follow-up of the participants indicated that consuming mushrooms on a regular basis reduces the risk of prostate cancer in men, and was especially significant in men aged 50 and older and in men whose diet consisted largely of meat and dairy products, with limited consumption of fruit and vegetables. Statistical analysis of the data (using the Cox proportional hazards model) indicated that regular mushroom consumption was related to a lower risk of prostate cancer regardless of how much fruit and vegetables, or meat and dairy products were consumed. Of the participants, 3.3% developed prostate cancer during the follow-up period. Participants who consumed mushrooms once or twice a week had an 8% lower risk of developing prostate cancer, compared to those who ate mushrooms less than once per week, while those who consumed mushrooms three or more times per week had a 17% lower risk than those who ate mushrooms less than once a week. According to Zhang, "mushrooms are a good source of vitamins, minerals and antioxidants, especially L-ergothioneine" -- which is believed to mitigate against oxidative stress, a cellular imbalance resulting from poor diet and lifestyle choices and exposure to environmental toxins that can lead to chronic inflammation that is responsible for chronic diseases such as cancer.
Abstract In vivo and in vitro evidence has shown that mushrooms have the potential to prevent prostate cancer. However, the relationship between mushroom consumption and incident prostate cancer in humans has never been investigated. In the present study, a total of 36,499 men, aged 40-79 years, who participated in the Miyagi Cohort Study in 1990 and in the Ohsaki Cohort Study in 1994 were followed for a median of 13.2 years. Data on mushroom consumption (categorized as <1, 1-2 and ≥3 times/week) was collected using a validated food frequency questionnaire. Cox proportional hazards regression analysis was used to estimate multivariate hazard ratios (HRs) and 95% confidence intervals (CIs) for prostate cancer incidence. During 574,397 person-years of follow-up, 1,204 (3.3%) cases of prostate cancer were identified. Compared to participants with mushroom consumption <1 time/week, frequent mushroom intake was associated with a decreased risk of prostate cancer (1-2 times/week: HRs [95% CIs] = 0.92 [0.81, 1.05]; ≥3 times/week: HRs [95% CIs] = 0.83 [0.70, 0.98]; p-trend = 0.023). This inverse relationship was especially obvious among participants aged ≥50 years and did not differ by clinical stage of cancer and intake of vegetables, fruit, meat and dairy products. The present study showed an inverse relationship between mushroom consumption and incident prostate cancer among middle-aged and elderly Japanese men, suggesting that habitual mushroom intake might help to prevent prostate cancer.
PERFORMANCE OF A NOVEL GENETIC RISK SCORE TO IDENTIFY RISK OF VENOUS THROMBOEMBOLISM IN PATIENTS WITH CARDIOMETABOLIC DISEASE
http://www.onlinejacc.org/content/75/11_Supplement_1/2194 Nicholas Marston, Giorgio Melloni, Yared Gurmu, Christina Lee, Frederick Kamanu, Carolina Roselli, Marc P. Bonaca, Ilaria Cavallari, Robert Giugliano, Benjamin M. Scirica, Deepak Bhatt, Philippe Gabriel Steg, Marc Cohen, Robert Storey, Terje Pedersen, Anthony C. Keech, Itamar Raz, Ofri Mosenzon, Eugene Braunwald, Steven Lubitz, Patrick T. Ellinor, Marc Sabatine and Christian T. Ruff
Background
Venous thromboembolism (VTE) is the third leading cause of CV mortality and has an established genetic predisposition. We tested the performance of a 10-SNP genetic risk score (GRS) for its ability to predict VTE in 3 TIMI trials.
Methods
We included patients from the FOURIER, PEGASUS-TIMI 54, and SAVOR-TIMI 53 trials (hx of ASCVD, MI, and DM, respectively) who consented for genetic testing. We created a VTE GRS based on 10 SNPs with established genome-wide significance. Patients were divided into tertiles of low, intermediate, and high genetic risk. Cox proportional hazards model was used to calculate hazard ratios for VTE across genetic risk categories, adjusted for age, sex, and ancestry. MI, CHD, and ischemic stroke were also assessed.
Results
A total of 31,669 patients were included in the analysis with a median follow-up up 2.4 years. 193 had a VTE event. There was a significant increased gradient of risk across VTE genetic risk categories (p-trend <0.0001, Figure). After adjustment, patients in the intermediate and high genetic risk categories had a 1.65- and 2.8-fold higher risk of an VTE (HR 1.65 [1.09-2.49], p=0.017; HR 2.80 [1.92-4.10], p=<0.0001). For each standard deviation increase in the GRS, there was a 38% increased risk of VTE (p<0.0001). The VTE risk score did not predict MI, CHD, or ischemic stroke
Conclusion
In a broad spectrum of patients with cardiometabolic disease, a 10-SNP VTE GRS is a strong predictor of venous thromboembolism, identifying patients who have ~3-fold risk of VTE. --------- Mentioned studies are all on PCSK9 inhibitors. FOURIER: evolocumab PEGASUS-TIMI 54: ticagrelor SAVOR-TIMI 53: saxagliptin What this means is that the improvement of PCSK9 inhibitors in cardiac events comes from a reduction in thrombosis rather than by lowering LDL cholesterol.
Serial Plasma Phospholipid Fatty Acids in the De Novo Lipogenesis Pathway and Total Mortality, Cause‐Specific Mortality, and Cardiovascular Diseases in the Cardiovascular Health Study - Nov 2019
Serial Plasma Phospholipid Fatty Acids in the De Novo Lipogenesis Pathway and Total Mortality, Cause‐Specific Mortality, and Cardiovascular Diseases in the Cardiovascular Health Study
Synthesized fatty acids (FAs) from de novo lipogenesis may affect cardiometabolic health, but longitudinal associations between serially measured de novo lipogenesis–related fatty acid biomarkers and mortality or cardiovascular disease (CVD) are not well established.
Methods and Results
We investigated longitudinal associations between de novo lipogenesis–related fatty acids with all‐cause mortality, cause‐specific mortality, and incident CVD among 3869 older US adults, mean (SD) age 75 (5) years and free of prevalent CVD at baseline. Levels of plasma phospholipid palmitic (16:0), palmitoleic (16:1n‐7), stearic (18:0), oleic acid (18:1n‐9), and other risk factors were serially measured at baseline, 6 years, and 13 years. All‐cause mortality, cause‐specific mortality, and incident fatal and nonfatal CVD were centrally adjudicated. Risk was assessed in multivariable‐adjusted Cox models with time‐varying FAs and covariates. During 13 years, median follow‐up (maximum 22.4 years), participants experienced 3227 deaths (1131 CVD, 2096 non‐CVD) and 1753 incident CVD events. After multivariable adjustment, higher cumulative levels of 16:0, 16:1n‐7, and 18:1n‐9 were associated with higher all‐cause mortality, with extreme‐quintile hazard ratios (95% CIs) of 1.35 (1.17–1.56), 1.40 (1.21–1.62), and 1.56 (1.35–1.80), respectively, whereas higher levels of 18:0 were associated with lower mortality (hazard ratio=0.76; 95% CI=0.66–0.88). Associations were generally similar for CVD mortality versus non‐CVD mortality, as well as total incident CVD. Changes in levels of 16:0 were positively, and 18:0 inversely, associated with all‐cause mortality (hazard ratio=1.23, 95% CI=1.08–1.41; and hazard ratio=0.78, 95% CI=0.68–0.90).
Conclusions
Higher long‐term levels of 16:0, 16:1n‐7, and 18:1n‐9 and changes in 16:0 were positively, whereas long‐term levels and changes in 18:0 were inversely, associated with all‐cause mortality in older adults.
Clinical Perspective
What Is New?
De novo lipogenesis (DNL), the liver's process of turning dietary starch, sugar, and protein into fat, is increasingly linked to insulin resistance, diabetes mellitus, and other metabolic conditions; yet, how the fatty acid (FA) products of DNL relate to all‐cause and cause‐specific mortality remains less clear.
We assessed how usual levels (measured serially over time) and changes in levels of DNL FA biomarkers measured in the blood, including palmitic acid (16:0), palmitoleic acid (16:1n‐7), stearic acid (18:0), and oleic acid (18:1n‐9), associate with death among older US adults through up to 22 years of follow‐up.
Higher usual‐term levels of 16:0, 16:1n‐7, and 18:1n‐9 associated with higher risks of all‐cause, CVD, and non‐CVD mortality—higher levels of 18:0 associated with lower risk, and assessing changes in levels over time, changes in 16:0 positively associated, whereas changes in 18:0 inversely associated, with risk of death.
What Are the Clinical Implications?
Higher long‐term blood levels of specific FA related to DNL may confer greater risk of mortality and impair cardiometabolic health, suggesting that both DNL and these FAs could represent targets to reduce such risk.
Observed protective associations for 18:0 are not consistent with limited previous studies, and further investigation of this FA is required.
Inflammation, Nutritional Ketosis, and Metabolic Disease. Steve Phinney lecture.
Inflammation, Nutritional Ketosis, and Metabolic Disease. Notes on lecture by Steve Phinney at Low Carb Conference, Nov 2018. Steve Phinney, MD, PhD Prof of Medicine emeritus, UC Davis Chief Medical Officer, Virta Health . A few definitions first: CRP, C reactive protein: is an annular, pentameric protein found in blood plasma, whose levels rise in response to inflammation. It is an acute-phase protein of hepatic origin that increases following interleukin-6 secretion by macrophages and T cells. Wikipedia Interleukin 6 (IL-6) is an interleukin that acts as both a pro-inflammatory cytokine and an anti-inflammatory myokine. In humans, it is encoded by the IL6 gene. IL-6 stimulates the inflammatory and auto-immune processes in many diseases such diabetes, as atherosclerosis,[27] depression,[28] Alzheimer's Disease,[29] systemic lupus erythematosus,[30]multiple myeloma,[31] prostate cancer,[32] Behçet's disease,[33] and rheumatoid arthritis.[34] The lecture Markers of inflammation This is a straight copy, from Minihane et al, British Journal of Nutrition (2015): ° blood cellular markers (e.g. total leucocytes, granulocytes and activated monocytes) • soluble mediators (cytokines and chemokines - TNF, IL-1, IL-6, IL-8, CC chemokine ligand 2 (CCL2), CCL3, CCL5), • adhesion molecules (vascular cell adhesion molecule-1, intercellular adhesion molecule-1, E-selectin) • adipokines (leptin, adiponectin) • acute-phase proteins (CRP, serum amyloid A, fibrinogen) • transcription factors such as NF- B and STAT3; • inflammatory enzymes such as cyclooxygenase (COX)-2, 5- lipoxygenase (LOX), 12-LOX, and matrix metalloproteinases (MMPs) Phinney then discussed work done by other people on:
The link between type 2 diabetes as an inflammatory disease;
Inflammatory mechanisms linking obesity and metabolic disease;
White blood cell count and coronary risk;
4. Early associations between CVD and inflammation;
CRP and IL-6 and coronary risk;
Study on rosuvastatin, a statin used to lower LDL and triglycerides, in men and women with elevated C-reactive protein, was stopped when hazard ratio went too high.
Bottom line: Highly significant primary prevention outcome, but unable to assign clear causality to either LDL or CRP reduction.
Anti-inflammatory Therapy with Canakinumab for Atherosclerotic Disease.
Paul M Ridker, Brendan M. Everett, et al., for the CANTOS Trial. Canakinumab use was associated with an increase in fatal sepsis, such that there was no significant reduction in overall mortality. Bottom Line: this highly focused anti-inflammatory pharmaceutical can reduce coronary mortality associated with a reduction in CRP, but the fatal side effects cancel any net therapeutic benefit. Phinney then posed the question, Can Nutrients Modulate Inflammation? Many nutrients are weak inflammation antagonists • Fish oil or DHA • Gamma-linolenic acid • Resveratrol In addition, Gamma-tocopherol is a potent anti-inflammatory. Gamma-tocopherol + DHA + Flavenoids can reduce CRP by 50% in 2 weeks. Phinney then turned to the meat of the lecture: Introduction to Nutritional Ketosis First he explained nutritional ketosis, as 1-3 mmol/L. (My comment: 0.5-3.0) (Compare with keto-acidosis, 10-20 mmol/L.) "Until recently, much of what is taught about ketones to health care providers is flawed or outright wrong. Most physicians have not been taught to differentiate between physiological ketones as a fuel source and the pathophysiology of diabetic keto-acidosis. In the past 5 years, our perspective and appreciation of BHB (Beta-hydroxybutyrate ketones) have changed dramatically. Ketones provide:
A Superior energy supply.
Hormonal activity.
They are involved in regulating oxidative stress and inflammation."
Phinney then discussed the new science of BOHB (I am not sure why he calls BHB BOHB. I could understand BHOB:BetaHydrOxyButyrate.) He described the work done by a group including John Newman (another lecturer I have already posted about) on the ability of BHB, an endogenous histone Deacetylase Inhibitor, to suppress oxidative stress. Reduced oxidative stress reduces aging and inflammation. Also BHB ketones might have a direct effect on insulin resistance. (Newman and Verdin, 2014.) How Oxidative Stress Translates to Inflammation. Production of pro-inflammatory isoprostanes from membrane arachidonate. Hope you understood that better than I did.😆 BOHB inhibits inflammatory gene expression. BOHB does not just reduce isoprostane production (prostaglandin-like compounds formed by ROS-perioxidation of essential fatty acids like ARA) • It intervenes at the regulatory level by blocking NLRP3 inflammasome-mediated inflammatory disease. (ELI5: I think this is saying that ketones can turn bad genes off. Tell me if I'm wrong.) Phinney then went on to discuss a study of the low fat diet versus the low carb diet and their effect on metabolic syndrome. Source: Forsythe et al. It showed that Carbohydrate Restriction has a More Favorable Impact on the Metabolic Syndrome than a Low Fat Diet͟. Lipids (2009) Details of study: N = 40 Demographics: • 40 overweight subjects with atherogenic dyslipidemia • Age: 18 – 55 years • BMI > 25 kg/m2 Method: • Outpatient for 12 weeks • Two randomly assigned groups: ○ LCD: eaten to satiety (reported 1500 kcal); 12% carb, 59% fat, 28% protein ○ Hypocaloric LFD: 1,500 kcal, 56% carb; 24% fat; 20% protein Additionally, the low carb diet had significantly higher weight loss. Also, all the markers for metabolic syndrome were significantly improved with the low carb diet, apart from blood pressure, and likewise for the markers of insulin resistance, where the difference in the two diets was dramatic. And there's more! **Total Saturated Fatty Acids was dramatically lower in LC than LF in serum, even though dietary intake was 3 times higher; – Likely because patients are so much better at oxidizing it.** And in 2008, Forsythe and friends found that: A well-formulated ketogenic diet has potent anti-inflammatory effects. LCKD vs LFD: 7 of 14 inflammation biomarkers significantly reduced. The 7 biomarkers that did not differ between the groups were: WBC CRP VEGF IL-6 EGF VCAM P-selectin Phinney then described the Virta ongoing clinical trial for reversing type 2 diabetes at Indiana University being led by Dr Sarah Hallberg. He described it as "intensive outpatient care", as all the patients have daily contact with a coach, are doing constant monitoring with biomarker tracking, see a physician regularly by telemed appointments, and have internet resources of recipes, videos, and guides. The average number of coach-patient interactions in the first 70 days is 3.1 per day Practically speaking this is Outpatient Intensive Care͟ Necessary for safe medication withdrawal. So, the IUH clinical trial under the principal investigator Dr Sarah Hallberg watches over the patients and monitors them closely. N = 262 living with T2D Location: Central Indiana Average Age: 54 Average BMI: 41 Average Weight: 257 lbs 67% female
After one year, 83% were still in the program. So, sustainable. Results: BHB went up sharply at beginning, to around 0.7 mmol/L, then gradually came down over the year to about 0.4 Body weight dropped during first 8 months. Then leveled off. White blood cell count reduced. C reactive protein down 39% by end of year. Diabetes reversed in 47% of the 262 initial cohort. Average A1c from 7.5 to 6.2 after a year. Patients off all meds except metformin. Changes in cardio vascular disease (CVD) risk factors: Big improvement in all but two. Improvements in: Triglycerides, HDL-C, Tri/HDL-C ratio, Small LDL-P, LDL size, ApoA1, ApoB/ApoA, VLDL-P, Sys Blood pressure, Dias Blood pressure, CRP, WBC. No change: LDL-P, Apo-B. So, stunning results for patients with respect to both their diabetes and heart disease risk. In summary: Ketones can serve as a fuel, resulting in good health for brain, heart and gut. Also, ketones act as signals, resulting in: Better Fat oxidation mitochondria improved health Reduction in inflammatory airway disease and asthma, Less oxidative stress, inflammation and cancer, Improved longevity and healthspan in mice. Conclusion: Nutritional Ketosis as an Anti-Inflammatory Therapy • There is no drug approved for chronic use that can deliver these potent anti-inflammatory benefits without side effects. • A well-formulated ketogenic diet has anti-inflammatory effects that are both very potent and broadly based – as opposed to a drug that is focused upon just one target enzyme or bioactive compound (e.g., IL-1 beta) • Given adequate instruction and support, most people who choose to try a well-formulated ketogenic diet can sustain it long-term and a majority will likely benefit. Miscellaneous facts from random places in lecture: can't remember where these gems fitted in! Arachidonic acid has 4 double bonds, vulnerable to R.O.S. Leptin sensitivity improves on low carb diet. Inflamed hypothalamus becomes insulin resistant, and leptin resistant. CRP is reduced on keto diet, but not immediately. Irritable Bowel Syndrome gets better on keto, as microbiome improved. Keto much much better than high grain low fat diet. Q and A: (each lecture was followed by 15 mins of Q and A ) If you are insulin resistant, don't eat too much protein. If you follow the no carb diet, you will need extra potassium. Diabetics with wounds that are inflamed and not curing: could be zinc deficiency. Patients were monitored in Holiday Inn, where they got their ketones up, but some ate too much protein. I think some one asked why the average BHB ketone level dropped from 0.7 at start, to 0.4 after a year, and Phinney said that they were strictly supervised to begin with, at the Holiday Inn, so they got into ketosis - defined as 0.5 mmol/L or more. However, when they returned home, their average ketone level dropped gradually, until they ended up after a year at 0.4 mmol/L, below the minimum 0.5 bar for ketosis. He said they were probably eating too much meat, which drove their BHB levels down. The consumption of omega 6, found in soybean oil, has increased dramatically over last few years. Billions of tons of soy produced, so some one has to eat it! (This is bad news.) All about economy. Comment from a doctor in the audience who had been treating his patients with low carb diet for past ten years: has cured lupus, rheumatoid arthritis, and psoriasis. Warning against high protein diet: in 1980s, people followed a liquid protein diet, and 60 of them died. After 3 months, sudden death. Likely they were sodium depleted. Wasted potassium. I read somewhere that sodium and potassium have to be balanced in the body, so if you are low on sodium, the body will discard potassium to match. This is likely what happen with liquid protein diet sudden deaths. TL;DR: Given constant TLC (tender loving care) patients such as diabetics can be helped a massive amount by following the ketogenic diet, and 54% of medications can be discontinued.
Association of Skipping Breakfast With Cardiovascular and All-Cause Mortality
Shuang Rong, Linda G. Snetselaar, Guifeng Xu, Yangbo Sun, Buyun Liu, Robert B. Wallace and Wei Bao
Abstract
Background Skipping breakfast is common among U.S. adults. Limited evidence suggests that skipping breakfast is associated with atherosclerosis and cardiovascular disease. Objectives The authors sought to examine the association of skipping breakfast with cardiovascular and all-cause mortality. Methods This is a prospective cohort study of a nationally representative sample of 6,550 adults 40 to 75 years of age who participated in the National Health and Nutrition Examination Survey III 1988 to 1994. Frequency of breakfast eating was reported during an in-house interview. Death and underlying causes of death were ascertained by linkage to death records through December 31, 2011. The associations between breakfast consumption frequency and cardiovascular and all-cause mortality were investigated by using weighted Cox proportional hazards regression models. Results Among the 6,550 participants (mean age 53.2 years; 48.0% male) in this study, 5.1% never consumed breakfast, 10.9% rarely consumed breakfast, 25.0% consumed breakfast some days, and 59.0% consumed breakfast every day. During 112,148 person-years of follow-up, 2,318 deaths occurred including 619 deaths from cardiovascular disease. After adjustment for age, sex, race/ethnicity, socioeconomic status, dietary and lifestyle factors, body mass index, and cardiovascular risk factors, participants who never consumed breakfast compared with those consuming breakfast everyday had hazard ratios of 1.87 (95% confidence interval: 1.14 to 3.04) for cardiovascular mortality and 1.19 (95% confidence interval: 0.99 to 1.42) for all-cause mortality. Conclusions In a nationally representative cohort with 17 to 23 years of follow-up, skipping breakfast was associated with a significantly increased risk of mortality from cardiovascular disease. Our study supports the benefits of eating breakfast in promoting cardiovascular health. Key Words
The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin FusteronJACC.org.
Received September 3, 2018.
Revision received December 8, 2018.
Accepted January 8, 2019.
2019 American College of Cardiology Foundation
Media:
CNN
https://www.cnn.com/2019/04/22/health/skipping-breakfast-cardiovascular-death-study/index.html Whether you eat breakfast might be linked with your risk of dying early from cardiovascular disease, according to a new study. Skipping breakfast was significantly associated with an increased risk of cardiovascular-related death, especially stroke-related death, in the study published in the Journal of the American College of Cardiology on Monday After a person's age, sex, race, socioeconomic status, diet, lifestyle, body mass index and disease status were taken into account, the study found that those who never had breakfast had a 87% higher risk of cardiovascular mortality compared with people who had breakfast every day, said Dr. Wei Bao, an assistant professor of epidemiology at the University of Iowa in Iowa City and senior author of the study."Breakfast is traditionally believed as the most or at least one of the most important meals of the day, but there are not much data available to say 'yes' or 'no' to this belief. Our paper is among the ones that provide evidence to support long-term benefits," Bao said. "There are a few cardiovascular risk factors -- for example diabetes, hypertension and lipid disorders," he said. "Our findings are in line with and supported by previous studies that consistently showed that skipping breakfast is related to those strong risk factors for cardiovascular death."Cardiovascular disease -- specifically heart disease and stroke -- is the leading cause of death in the world, accounting for a combined 15.2 million deaths in 2016, according to the World Health Organization. Heart disease is the leading cause of death in the United States.
Skipping breakfast and cardiovascular death
The study involved data from 1988 to 1994 on 6,550 US adults, aged 40 to 75, who reported how often they ate breakfast in the National Health and Nutrition Examination Survey.The survey data generally let respondents define what meal would be considered breakfast. Separate data was analyzed to determine the adults' health status through 2011. All told, 2,318 deaths occurred during an average follow-up period of 18.8 years, including 619 from cardiovascular disease.The researchers took a close look at how often each person consumed breakfast and at mortality, specifically whether a death was related to cardiovascular health. In the data, breakfast was defined as...Of those adults, 5.1% reported never consuming breakfast; 10.9% rarely ate breakfast; 25% had breakfast on some days; and 59% had breakfast every day.Compared with those who consumed breakfast every day, adults who never did so had a higher risk of heart disease-related death and stroke-related death, according to the study.Those associations were found to be significant and independent of socioeconomic status, body mass index and cardiovascular risk factors, the researchers noted. "To the best of our knowledge, this is the first prospective analysis of skipping breakfast and risk of cardiovascular mortality," they wrote.The study had some limitations, including that the data did not include information about what types of foods or drinks were consumed for breakfast and whether a person's breakfast consumption patterns changed between 1994 and when the followup mortality data was collected.Most important, the study found only an association between skipping breakfast and risk of early death, not that skipping breakfast specifically causes any such outcomes. More research is needed to determine whether missing the meal actually could shorten life expectancy and why such an association exists.
The complexities of skipping breakfast
In general, the study noted that skipping breakfast has been associated with increased risk of obesity, elevated cholesterol or fats in the blood, high blood pressure, Type 2 diabetes, metabolic syndrome and heart disease.A study published in the journal Circulation in 2013 found that breakfast was associated with a significantly lower risk of coronary heart disease in men. The new study "was fairly well done," said Krista Varady, associate professor of nutrition at the University of Illinois, Chicago, who was not involved in the research."However, the major issue is that the subjects who regularly skipped breakfast also had the most unhealthy lifestyle habits," she said. "Specifically, these people were former smokers, heavy drinkers, physically inactive, and also had poor diet quality and low family income." All of those factors put people at a much higher risk for cardiovascular disease. "I realize that the study attempted to control for these confounders, but I think it's hard to tease apart breakfast skipping from their unhealthy lifestyle in general," Varady said. Some people might skip breakfast as part of an intermittent fasting routine, but the breakfast skipping in the study and breakfast skipping during intermittent fasting are two different concepts and practices, said Valter Longo, a professor of biological sciences at the University of Southern California in Los Angeles and director of the USC Longevity Institute, who was not involved in the new research. Intermittent fasting occurs when you cycle between long periods of not eating and then regular eating, helping restrict your calorie intake. Some studies, several involving animals, suggest that intermittent fasting can reduce the risk of obesity and its related diseases, such as non-alcoholic fatty liver disease, diabetes and cancer. To connect the study's findings to intermittent fasting, Longo warns "be careful.""There are very good ways to do intermittent fasting and potentially very bad ways to do intermittent fasting," Longo said."But certainly, that's an interesting thing to keep in mind, that A: Maybe it's better to stick with 12 hours or 13 hours of fasting and that's it," he said. "Or B: If you need to do 16 hours, try to consider skipping dinner and not breakfast or lunch."
Inflammation, Nutritional Ketosis, and Metabolic Disease. Steve Phinney lecture.
Inflammation, Nutritional Ketosis, and Metabolic Disease. Steve Phinney lecture. Inflammation, Nutritional Ketosis, and Metabolic Disease. Notes on lecture by Steve Phinney at Low Carb Conference, Nov 2018. Steve Phinney, MD, PhD Prof of Medicine emeritus, UC Davis Chief Medical Officer, Virta Health . A few definitions first: CRP, C reactive protein: is an annular, pentameric protein found in blood plasma, whose levels rise in response to inflammation. It is an acute-phase protein of hepatic origin that increases following interleukin-6 secretion by macrophages and T cells. Wikipedia Interleukin 6 (IL-6) is an interleukin that acts as both a pro-inflammatory cytokine and an anti-inflammatory myokine. In humans, it is encoded by the IL6 gene. IL-6 stimulates the inflammatory and auto-immune processes in many diseases such diabetes, as atherosclerosis, depression, Alzheimer's Disease, systemic lupus erythematosus, multiple myeloma, prostate cancer, Behçet's disease,and rheumatoid arthritis. Resveratrol is a stilbenoid, a type of natural phenol, and a phytoalexin produced by several plants in response to injury or, when the plant is under attack by pathogens such as bacteria or fungi. Sources of resveratrol in food include the skin of grapes, blueberries, raspberries, mulberries, and peanuts. The lecture Markers of inflammation This is a straight copy, from Minihane et al, British Journal of Nutrition (2015): ° blood cellular markers (e.g. total leucocytes, granulocytes and activated monocytes) • soluble mediators (cytokines and chemokines - TNF, IL-1, IL-6, IL-8, CC chemokine ligand 2 (CCL2), CCL3, CCL5), • adhesion molecules (vascular cell adhesion molecule-1, intercellular adhesion molecule-1, E-selectin) • adipokines (leptin, adiponectin) • acute-phase proteins (CRP, serum amyloid A, fibrinogen) • transcription factors such as NF- B and STAT3; • inflammatory enzymes such as cyclooxygenase (COX)-2, 5- lipoxygenase (LOX), 12-LOX, and matrix metalloproteinases (MMPs) Phinney then discussed work done by other people on:
The link between type 2 diabetes as an inflammatory disease;
Inflammatory mechanisms linking obesity and metabolic disease;
White blood cell count and coronary risk;
4. Early associations between CVD and inflammation;
CRP and IL-6 and coronary risk;
Study on rosuvastatin, a statin used to lower LDL and triglycerides, in men and women with elevated C-reactive protein, was stopped when hazard ratio went too high.
Bottom line: Highly significant primary prevention outcome, but unable to assign clear causality to either LDL or CRP reduction.
Anti-inflammatory Therapy with Canakinumab for Atherosclerotic Disease.
Paul M Ridker, Brendan M. Everett, et al., for the CANTOS Trial. Canakinumab use was associated with an increase in fatal sepsis, such that there was no significant reduction in overall mortality. Bottom Line: this highly focused anti-inflammatory pharmaceutical can reduce coronary mortality associated with a reduction in CRP, but the fatal side effects cancel any net therapeutic benefit. Phinney then posed the question, Can Nutrients Modulate Inflammation? Many nutrients are weak inflammation antagonists • Fish oil or DHA • Gamma-linolenic acid • Resveratrol In addition, Gamma-tocopherol is a potent anti-inflammatory. Gamma-tocopherol + DHA + Flavenoids can reduce CRP by 50% in 2 weeks. Phinney then turned to the meat of the lecture: Introduction to Nutritional Ketosis First he explained nutritional ketosis, as 1-3 mmol/L. (My comment: 0.5-3.0) (Compare with keto-acidosis, 10-20 mmol/L.) "Until recently, much of what is taught about ketones to health care providers is flawed or outright wrong. Most physicians have not been taught to differentiate between physiological ketones as a fuel source and the pathophysiology of diabetic keto-acidosis. In the past 5 years, our perspective and appreciation of BHB (Beta-hydroxybutyrate ketones) have changed dramatically. Ketones provide:
A Superior energy supply.
Hormonal activity.
They are involved in regulating oxidative stress and inflammation."
Phinney then discussed the new science of BOHB (I am not sure why he calls BHB BOHB. I could understand BHOB:BetaHydrOxyButyrate.) He described the work done by a group including John Newman (another lecturer I have already posted about) on the ability of BHB, an endogenous histone Deacetylase Inhibitor, to suppress oxidative stress. Reduced oxidative stress reduces aging and inflammation. Also BHB ketones might have a direct effect on insulin resistance. (Newman and Verdin, 2014.) How Oxidative Stress Translates to Inflammation. Production of pro-inflammatory isoprostanes from membrane arachidonate. Hope you understood that better than I did.😆 BOHB inhibits inflammatory gene expression. BOHB does not just reduce isoprostane production (prostaglandin-like compounds formed by ROS-perioxidation of essential fatty acids like ARA) • It intervenes at the regulatory level by blocking NLRP3 inflammasome-mediated inflammatory disease. (ELI5: I think this is saying that ketones can turn bad genes off. Tell me if I'm wrong.) Phinney then went on to discuss a study of the low fat diet versus the low carb diet and their effect on metabolic syndrome. Source: Forsythe et al. It showed that Carbohydrate Restriction has a More Favorable Impact on the Metabolic Syndrome than a Low Fat Diet͟. Lipids (2009) Details of study: N = 40 Demographics: • 40 overweight subjects with atherogenic dyslipidemia • Age: 18 – 55 years • BMI > 25 kg/m2 Method: • Outpatient for 12 weeks • Two randomly assigned groups: ○ LCD: eaten to satiety (reported 1500 kcal); 12% carb, 59% fat, 28% protein ○ Hypocaloric LFD: 1,500 kcal, 56% carb; 24% fat; 20% protein Additionally, the low carb diet had significantly higher weight loss. Also, all the markers for metabolic syndrome were significantly improved with the low carb diet, apart from blood pressure, and likewise for the markers of insulin resistance, where the difference in the two diets was dramatic. And there's more! **Total Saturated Fatty Acids was dramatically lower in LC than LF in serum, even though dietary intake was 3 times higher; – Likely because patients are so much better at oxidizing it.** And in 2008, Forsythe and friends found that: A well-formulated ketogenic diet has potent anti-inflammatory effects. LCKD vs LFD: 7 of 14 inflammation biomarkers significantly reduced. The 7 biomarkers that did not differ between the groups were: WBC CRP VEGF IL-6 EGF VCAM P-selectin Phinney then described the Virta ongoing clinical trial for reversing type 2 diabetes at Indiana University being led by Dr Sarah Hallberg. He described it as "intensive outpatient care", as all the patients have daily contact with a coach, are doing constant monitoring with biomarker tracking, see a physician regularly by telemed appointments, and have internet resources of recipes, videos, and guides. The average number of coach-patient interactions in the first 70 days is 3.1 per day Practically speaking this is Outpatient Intensive Care͟ Necessary for safe medication withdrawal. So, the IUH clinical trial under the principal investigator Dr Sarah Hallberg watches over the patients and monitors them closely. N = 262 living with T2D Location: Central Indiana Average Age: 54 Average BMI: 41 Average Weight: 257 lbs 67% female
After one year, 83% were still in the program. So, sustainable. Results: BHB went up sharply at beginning, to around 0.7 mmol/L, then gradually came down over the year to about 0.4 Body weight dropped during first 8 months. Then leveled off. White blood cell count reduced. C reactive protein down 39% by end of year. Diabetes reversed in 47% of the 262 initial cohort. Average A1c from 7.5 to 6.2 after a year. Patients off all meds except metformin. Changes in cardio vascular disease (CVD) risk factors: Big improvement in all but two. Improvements in: Triglycerides, HDL-C, Tri/HDL-C ratio, Small LDL-P, LDL size, ApoA1, ApoB/ApoA, VLDL-P, Sys Blood pressure, Dias Blood pressure, CRP, WBC. No change: LDL-P, Apo-B. So, stunning results for patients with respect to both their diabetes and heart disease risk. In summary: Ketones can serve as a fuel, resulting in good health for brain, heart and gut. Also, ketones act as signals, resulting in: Better Fat oxidation mitochondria improved health Reduction in inflammatory airway disease and asthma, Less oxidative stress, inflammation and cancer, Improved longevity and healthspan in mice. Conclusion: Nutritional Ketosis as an Anti-Inflammatory Therapy • There is no drug approved for chronic use that can deliver these potent anti-inflammatory benefits without side effects. • A well-formulated ketogenic diet has anti-inflammatory effects that are both very potent and broadly based – as opposed to a drug that is focused upon just one target enzyme or bioactive compound (e.g., IL-1 beta) • Given adequate instruction and support, most people who choose to try a well-formulated ketogenic diet can sustain it long-term and a majority will likely benefit. Miscellaneous facts from random places in lecture: can't remember where these gems fitted in! Arachidonic acid has 4 double bonds, vulnerable to R.O.S. Leptin sensitivity improves on low carb diet. Inflamed hypothalamus becomes insulin resistant, and leptin resistant. CRP is reduced on keto diet, but not immediately. Irritable Bowel Syndrome gets better on keto, as microbiome improved. Keto much much better than high grain low fat diet. Q and A: (each lecture was followed by 15 mins of Q and A ) If you are insulin resistant, don't eat too much protein. If you follow the no carb diet, you will need extra potassium. Diabetics with wounds that are inflamed and not curing: could be zinc deficiency. Patients were monitored in Holiday Inn, where they got their ketones up, but some ate too much protein. I think some one asked why the average BHB ketone level dropped from 0.7 at start, to 0.4 after a year, and Phinney said that they were strictly supervised to begin with, at the Holiday Inn, so they got into ketosis - defined as 0.5 mmol/L or more. However, when they returned home, their average ketone level dropped gradually, until they ended up after a year at 0.4 mmol/L, below the minimum 0.5 bar for ketosis. He said they were probably eating too much meat, which drove their BHB levels down. The consumption of omega 6, found in soybean oil, has increased dramatically over last few years. Billions of tons of soy produced, so some one has to eat it! (This is bad news.) All about economy. Comment from a doctor in the audience who had been treating his patients with low carb diet for past ten years: has cured lupus, rheumatoid arthritis, and psoriasis. Warning against high protein diet: in 1980s, people followed a liquid protein diet, and 60 of them died. After 3 months, sudden death. Likely they were sodium depleted. Wasted potassium. I read somewhere that sodium and potassium have to be balanced in the body, so if you are low on sodium, the body will discard potassium to match. This is likely what happen with liquid protein diet sudden deaths. TL;DR: Given constant TLC (tender loving care) patients such as diabetics can be helped a massive amount by following the ketogenic diet, and 54% of medications can be discontinued. Cross posted on ketoscience
Toward a Neurology of Loneliness - The neurological effects of prolonged social isolation
Found this while researching the neurological effects of chronic (severe) social isolation. It's the most thorough overview I've found and demonstrates in horrifying detail how it's really one of the worst things you can do to yourself. https://cacioppo.squarespace.com/s/toward-a-neurology-of-loneliness-916y.pdf Not as long as it seems, 20 pages are tables on animal studies and the effects of depression found, along with a long reference section. Page 2 in particular has a good overview of the changes that occur in a socially isolated brain. Some key excerpts demonstrating just how bad the effects are compared to other detrimental factors, references removed to reduced size: In 2010, a meta-analysis revealed that the odds ratio for increased mortality for loneliness is 1.45, which is approximately double the odds ratio for increased mortality for obesity and quadruple the odds ratio for air pollution
Results showed that loneliness was associated with increased mortality risk over a 6-year period and that neither health behaviors nor objective features of social relationships (e.g., marital status, proximity to friends or family) could explain the association between loneliness and mortality. Several studies also indicate that loneliness is a risk factor for cognitive decline and dementia. For instance, Gow et al. (2007) investigated the correlates of changes in mental ability of 488 individuals from the Lothian Birth Cohort Study who were tested at ages 11 and 79. Among the variables tested were loneliness, social support, and objective social isolation, the last measured using a social network index (e.g., presence of significant others, number of significant others). After controlling for age, IQ, gender, years of education, and social class, only loneliness was associated significantly with changes in IQ. However, Gow et al. did not address the possibility that loneliness is a consequence rather than a predictor of cognitive decline.
Two recent longitudinal studies do speak to this question. Results at the 10-year follow-up assessment revealed that two biological measures and loneliness independently predicted cognitive decline. Cox proportional hazards models that controlled for age, sex, and education indicated that loneliness significantly increased the risk of clinical Alzheimer’s disease, and this association was unchanged when objective social isolation and other demographic and health-related factors were included as covariates.
Investigations designed to identify the mechanisms underlying the association between loneliness and mortality have found that loneliness is associated not only with increased risk for age-related cognitive decline and dementia but also with increased sleep fragmentation, increased hypothalamic pituitary adrenocortical (HPA) activity, altered gene expression indicative of decreased inflammatory control and increased glucocorticoid insensitivity, ), increased inflammation, elevated vascular resistance and blood pressure, higher rates of metabolic syndrome, and diminished immunity. Loneliness has also been associated with changes in psychological states that can contribute to morbidity and mortality, including increased depressive symptomatology, lower subjective wellbeing, , heightened vigilance for social threats, and decreased executive functioning.
A section on neurogenesis begins on page 29. Supporting articles: Maslow Be Damned: How Social Belonging Trumps Everything (Based on the work of Thomas Joiner, who has written what are possibly the best books on suicide) http://theviewfromhell.blogspot.com/2011/05/maslow-be-damned-how-social-belonging.html (A thread on this was posted before) One is the deadliest number: the detrimental effects of social isolation on cerebrovascular diseases and cognition. http://www.ncbi.nlm.nih.gov/pubmed/25537401 Suicidal Thoughts 10 Times More Likely in Adults With Asperger’s http://psychcentral.com/news/2014/10/13/suicidal-thoughts-10-times-more-likely-in-adults-with-aspergers/76016.html And there's much more out there. I recommend this book: http://www.amazon.com/Loneliness-Human-Nature-Social-Connection/dp/0393335283 It's really one of the worst things you can do to yourself. The last is particularly pertinent. Think about a person with psychosis and whether you could live the rest of your life like that, how most people would respond if asked that. What would cause a disorder to have a suicide rate even higher than that of psychosis? Humans weren't meant to be alone. In a way we're the most social, the most socially complex and cooperative, animals around, so much of our brain developed and is dedicated toward social behavior; when you take that away, everything can just fall apart. It also skews your perceptions and can lead to a self-reinforcing cycle. If you have a disorder that makes social interaction/relationships painful or difficult, get help, start working on it, as soon as possible. As flawed, inadequate, as other people and the world may seem, it can't be anywhere near as bad as how you can end up after isolating yourself to the extent I did. Even if you feel happy now, you really won't understand just how bad it can get until you have no one in your life, which can occur after you leave your parents and school, have a job with little or no meaningful social interaction; and if you've never experienced a healthy, fulfilling, social life/relationships, you really won't have a good reference point for how differently you could have felt. There are so many ways that relationships help develop yourself, so many things you can miss out on. There really are good people in the world you can find, even if you feel alienated, extremely uncommon, and have thought patterns, ideologies, that reinforce your negative view. Try to be more forgiving and explore seeing things in a new way. I'll share my own experience in the comments and answer any questions if anyone's interested. I'm already trying to get as much help as I can, but it's probably going to be something that will haunt me and that I'll struggle with for the rest of my life, who knows how much permanent damage I may have done. An idea that's interested me is being the subject of a university study/research into the effects of prolonged severe social isolation. I have no idea how to go about this or whether anyone would be interested, if it's redundant and already been done before.
Analyzing survival data split by categories of time variable
I hope that this will be clear. Please feel free to ask clarifying questions if not!
I have been asked to perform a Cox analysis on data where my main question is whether or not the hazard function associated with the exposure differs across age groups. My age groups are 0-16, 17-24 and 25+. My exposure variable is three levels (0, 1 or 2). The time scale I am using for my analyses is age.
Currently my data are set as follows:
stset age, id(id) failure(failed)
I have stsplit my data as follows:
stsplit agechunks, at(17 25)
I think I might be misinterpreting the results of the Cox regression I then performed using the following code (I fvset the referent category for agechunks to 0)
My question is why Stata is not producing standard errors for certain estimates here. It seems to me it might have something to do with the fact that there is a collinearity issue, since I’m splitting my data on my time scale variable, but I don’t know the way around that issue. Further, I am a little confused as to what the hazard ratios generated are being compared to. I might assume they were being compared to the estimates with no SEs, but normally I would be able to get around that using reset and specifying allbaselevels, and there would be no HRs generated.
Clearly I'm extremely confused. Does anyone have an explanation, a solution, or any insight?
I am using the cox's proportional hazards model, and one of my hazard ratio is 6.31e-09 The whole line reads like this: Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] 6.31e-09 2.17e-09 -54.98 0.000 3.22e-09 1.24e-08 What does this mean?
More Evidence Links Air Pollution to Dementia-BMJ open-Medscape
A new study has found a positive association between residential levels of air pollution and a diagnosis of dementia. "We found that older patients across London (UK) who were living in areas with higher air pollution were more likely to be diagnosed with dementia in subsequent years," lead author Iain Carey, MD, St. George's, University of London, told Medscape Medical News. He noted that most of the epidemiological evidence linking long-term concentrations of air pollution to adverse health effects has mainly focused on cardiovascular disease. However, possible associations between traffic pollution and dementia have been shown in a few recent population studies, notably a large Canadian study published in The Lancet. The current findings are the first evidence from the United Kingdom. Although acknowledging that causation cannot be established by these observational studies, Carey suggested, "these findings are another reason to minimize exposure to air pollution." Senior study author Frank Kelly, MD, King's College London, suggested exposure could be reduced by avoiding travel during rush hour. "Indoors, you can minimize pollution by not burning candles, open fires, have good ventilation/extraction when cooking and cleaning," he added. "Face masks do not usually work unless they are an extremely good fit to the face and have good filters in place — the most expensive." The study was published online on September 18 in BMJ Open. For the retrospective cohort study, researchers used data from the Clinical Practice Research Datalink (CPRD), a large, validated primary care database that has been collecting anonymous patient data from participating UK general practices since 1987. The current study involved 130,978 adults aged 50 to 79 years in 2005 with no recorded history of dementia or care home residence from 75 general practices in Greater London with clinical data available up to 2013. During 2004, the average annual concentrations of nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter ≤ 2.5 µm (PM2.5), and ozone (O3) were estimated from dispersion models, and traffic intensity, distance from major road, and night-time noise levels were estimated at the postcode level. All pollution measures were anonymously linked to clinical data using residential postcode. Hazard ratios (HRs) from Cox models were adjusted for age, sex, ethnicity, smoking, and body mass index, with further adjustments explored for area deprivation and comorbidity. Results showed that 2181 people (1.7%) received an incident diagnosis of dementia (39% mentioned Alzheimer's disease, 29% vascular dementia). There was a positive exposure–response relationship between dementia and all measures of air pollution except O3, which was not readily explained by further adjustment. Adults living in areas with the highest fifth of NO2 concentration (> 41.5 µg/m3) versus the lowest fifth (< 31.9 µg/m3) were at a higher risk of dementia (HR, 1.40; 95% CI, 1.12 - 1.74). Increases in dementia risk were also observed with PM2.5, Associations were more consistent for Alzheimer's disease than vascular dementia. "Since this is an observational study, it only tells us there may be a possible link between air pollution and dementia. There may be many factors involved, only some of which we were able to account for," Carey commented. "We also have to consider our limitations, such as uncertainty around personal exposure, where we assume that traffic pollution levels at somebody's address can adequately represent their long-term exposure," he added. "There are also valid concerns about the under-diagnosing of dementia on electronic patient records." Accounts for 7% of Dementia Cases? Despite these limitations, the researchers attempted to quantify the risk. "We calculated how much of the dementia was attributable to pollution (NO2), by assuming what would happen if all patients in the study had instead been estimated to have been exposed at the levels associated with the bottom 20%," Carey explained. "The difference between this theoretical scenario and what we observed gave us an attributable risk of 7%." "Of course, this estimate only applies to our study and is not easily extrapolated elsewhere where exposure levels differ,” he cautioned. “However, in the large Canadian study by Chen and colleagues, they estimated a similar 6% for the combined effects of NO2 and PM2.5." Asked to comment for Medscape Medical News, an author of that Canadian study, Ray Copes, MD, Public Health Ontario, Toronto, Canada, called these new findings, "a significant addition to the evidence implicating exposure to traffic-related air pollution as a risk factor for dementia." He said strengths of the study included that the authors were able to disentangle the effects of noise from air pollution and the use of individual level data on smoking status and body mass index, "things we were unable to do with our study." The finding that only NO2 and PM2.5 remained significant in multi-pollutant models including noise and other potential confounders is not surprising "but most worthwhile to see confirmed," he said. "Unfortunately, as is the case with other studies, specific data on the smallest particles included in PM2.5, the ultrafines, were not available," he noted. "It is possible that an even stronger link to dementia might be found with ultrafine particles, which are a component of fresh diesel exhaust." In the BMJ Open article, Carey and colleagues conclude: "With the future global burden of dementia likely to be substantial, further epidemiological work is urgently needed to confirm and understand better recent findings linking air pollution to dementia." "Our results suggest both regional and urban background pollutants may be as important as near-traffic pollutants. Future large-scale studies will need to rely on improved recording and linkage of dementia diagnoses across electronic systems, particularly Alzheimer's disease, where multiple sources can improve diagnostic accuracy," they write. "Since exposure is lifelong, and most cases are diagnosed in later life, historical data are also ideally required to better estimate cumulative exposure over preceding decades," they conclude. The study was supported by the UK Natural Environment Research Council, Medical Research Council, Economic and Social Research Council, Department for Environment, Food and Rural Affairs, and Department of Health. The research was also partly funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards at King's College London in partnership with Public Health England and Imperial College London. The authors have reported no financial disclosures. BMJ Open. Published online September 18, 2018. Link: https://www.medscape.com/viewarticle/902715
Toward a Neurology of Loneliness - The neurological effects of prolonged social isolation (x-post r/nootropics)
Cross posting to this to multiple relevant subreddits and sites because after much research, analysis, introspection, and rumination I came to realize that it's really been on the proximal cause of the majority of problems in my life and my suffering. This is a key factor that many factor that many may be resistant to accept, like I was, and that many are unaware of just how significant it is. Those interested in related fields may also find this fascinating. The comments in the original thread are worth reading: https://www.np.reddit.com/Nootropics/comments/3u999j/toward_a_neurology_of_loneliness_the_neurological Found this while researching the neurological effects of chronic (severe) social isolation. It's the most thorough overview I've found and demonstrates in horrifying detail how it's really one of the worst things you can do to yourself. https://cacioppo.squarespace.com/s/toward-a-neurology-of-loneliness-916y.pdf Not as long as it seems, 20 pages are tables on animal studies and the effects of depression found, along with a long reference section. Page 2 in particular has a good overview of the changes that occur in a socially isolated brain. Some key excerpts demonstrating just how bad the effects are compared to other detrimental factors, references removed to reduced size: In 2010, a meta-analysis revealed that the odds ratio for increased mortality for loneliness is 1.45, which is approximately double the odds ratio for increased mortality for obesity and quadruple the odds ratio for air pollution
Results showed that loneliness was associated with increased mortality risk over a 6-year period and that neither health behaviors nor objective features of social relationships (e.g., marital status, proximity to friends or family) could explain the association between loneliness and mortality. Several studies also indicate that loneliness is a risk factor for cognitive decline and dementia. For instance, Gow et al. (2007) investigated the correlates of changes in mental ability of 488 individuals from the Lothian Birth Cohort Study who were tested at ages 11 and 79. Among the variables tested were loneliness, social support, and objective social isolation, the last measured using a social network index (e.g., presence of significant others, number of significant others). After controlling for age, IQ, gender, years of education, and social class, only loneliness was associated significantly with changes in IQ. However, Gow et al. did not address the possibility that loneliness is a consequence rather than a predictor of cognitive decline.
Two recent longitudinal studies do speak to this question. Results at the 10-year follow-up assessment revealed that two biological measures and loneliness independently predicted cognitive decline. Cox proportional hazards models that controlled for age, sex, and education indicated that loneliness significantly increased the risk of clinical Alzheimer’s disease, and this association was unchanged when objective social isolation and other demographic and health-related factors were included as covariates.
Investigations designed to identify the mechanisms underlying the association between loneliness and mortality have found that loneliness is associated not only with increased risk for age-related cognitive decline and dementia but also with increased sleep fragmentation, increased hypothalamic pituitary adrenocortical (HPA) activity, altered gene expression indicative of decreased inflammatory control and increased glucocorticoid insensitivity, ), increased inflammation, elevated vascular resistance and blood pressure, higher rates of metabolic syndrome, and diminished immunity. Loneliness has also been associated with changes in psychological states that can contribute to morbidity and mortality, including increased depressive symptomatology, lower subjective wellbeing, , heightened vigilance for social threats, and decreased executive functioning.
A section on neurogenesis begins on page 29. Supporting articles: Maslow Be Damned: How Social Belonging Trumps Everything (Based on the work of Thomas Joiner, who has written what are possibly the best books on suicide) http://theviewfromhell.blogspot.com/2011/05/maslow-be-damned-how-social-belonging.html (A thread on this was posted before) One is the deadliest number: the detrimental effects of social isolation on cerebrovascular diseases and cognition. http://www.ncbi.nlm.nih.gov/pubmed/25537401 Suicidal Thoughts 10 Times More Likely in Adults With Asperger’s http://psychcentral.com/news/2014/10/13/suicidal-thoughts-10-times-more-likely-in-adults-with-aspergers/76016.html And there's much more out there. It's really one of the worst things you can do to yourself. The last is particularly pertinent. Think about a person with psychosis and whether you could live the rest of your life like that, how most people would respond if asked that. What would cause a disorder to have a suicide rate even higher than that of psychosis? Humans weren't meant to be alone. In a way we're the most social, the most socially complex and cooperative, animals around, so much of our brain developed and is dedicated toward social behavior; when you take that away, everything can just fall apart. It also skews your perceptions and can lead to a self-reinforcing cycle. If you have a disorder that makes social interaction/relationships painful or difficult, get help, start working on it, as soon as possible. As flawed, inadequate, as other people and the world may seem, it can't be anywhere near as bad as how you can end up after isolating yourself to the extent I did. Even if you feel happy now, you really won't understand just how bad it can get until you have no one in your life, which can occur after you leave your parents and school, have a job with little or no meaningful social interaction; and if you've never experienced a healthy, fulfilling, social life/relationships, you really won't have a good reference point for how differently you could have felt. There are so many ways that relationships help develop yourself, so many things you can miss out on. There really are good people in the world you can find, even if you feel alienated, extremely uncommon, and have thought patterns, ideologies, that reinforce your negative view. Try to be more forgiving and explore seeing things in a new way. I'll share my own experience in the comments and answer any questions if anyone's interested. I essentially ended up as a hikikomori. I'm already trying to get as much help as I can, but it's probably going to be something that will haunt me and that I'll struggle with for the rest of my life, who knows how much permanent damage I may have done. An idea that's interested me is being the subject of a university study/research into the effects of prolonged severe social isolation. I have no idea how to go about this or whether anyone would be interested, if it's redundant and already been done before.
Toward a Neurology of Loneliness - The neurological effects of prolonged social isolation
Cross posting to this to multiple relevant subreddits and sites because after much research, analysis, introspection, and rumination I came to realize that it's really been on the proximal cause of the majority of problems in my life and my suffering. This is a key factor that many factor that many may be resistant to accept, like I was, and that many are unaware of just how significant it is. Those interested in related fields may also find this fascinating. The comments in the original thread are worth reading: https://www.np.reddit.com/Nootropics/comments/3u999j/toward_a_neurology_of_loneliness_the_neurological Found this while researching the neurological effects of chronic (severe) social isolation. It's the most thorough overview I've found and demonstrates in horrifying detail how it's really one of the worst things you can do to yourself. https://cacioppo.squarespace.com/s/toward-a-neurology-of-loneliness-916y.pdf Not as long as it seems, 20 pages are tables on animal studies and the effects of depression found, along with a long reference section. Page 2 in particular has a good overview of the changes that occur in a socially isolated brain. Some key excerpts demonstrating just how bad the effects are compared to other detrimental factors, references removed to reduced size: In 2010, a meta-analysis revealed that the odds ratio for increased mortality for loneliness is 1.45, which is approximately double the odds ratio for increased mortality for obesity and quadruple the odds ratio for air pollution
Results showed that loneliness was associated with increased mortality risk over a 6-year period and that neither health behaviors nor objective features of social relationships (e.g., marital status, proximity to friends or family) could explain the association between loneliness and mortality. Several studies also indicate that loneliness is a risk factor for cognitive decline and dementia. For instance, Gow et al. (2007) investigated the correlates of changes in mental ability of 488 individuals from the Lothian Birth Cohort Study who were tested at ages 11 and 79. Among the variables tested were loneliness, social support, and objective social isolation, the last measured using a social network index (e.g., presence of significant others, number of significant others). After controlling for age, IQ, gender, years of education, and social class, only loneliness was associated significantly with changes in IQ. However, Gow et al. did not address the possibility that loneliness is a consequence rather than a predictor of cognitive decline.
Two recent longitudinal studies do speak to this question. Results at the 10-year follow-up assessment revealed that two biological measures and loneliness independently predicted cognitive decline. Cox proportional hazards models that controlled for age, sex, and education indicated that loneliness significantly increased the risk of clinical Alzheimer’s disease, and this association was unchanged when objective social isolation and other demographic and health-related factors were included as covariates.
Investigations designed to identify the mechanisms underlying the association between loneliness and mortality have found that loneliness is associated not only with increased risk for age-related cognitive decline and dementia but also with increased sleep fragmentation, increased hypothalamic pituitary adrenocortical (HPA) activity, altered gene expression indicative of decreased inflammatory control and increased glucocorticoid insensitivity, ), increased inflammation, elevated vascular resistance and blood pressure, higher rates of metabolic syndrome, and diminished immunity. Loneliness has also been associated with changes in psychological states that can contribute to morbidity and mortality, including increased depressive symptomatology, lower subjective wellbeing, , heightened vigilance for social threats, and decreased executive functioning.
A section on neurogenesis begins on page 29. Supporting articles: Maslow Be Damned: How Social Belonging Trumps Everything (Based on the work of Thomas Joiner, who has written what are possibly the best books on suicide) http://theviewfromhell.blogspot.com/2011/05/maslow-be-damned-how-social-belonging.html (A thread on this was posted before) One is the deadliest number: the detrimental effects of social isolation on cerebrovascular diseases and cognition. http://www.ncbi.nlm.nih.gov/pubmed/25537401 Suicidal Thoughts 10 Times More Likely in Adults With Asperger’s http://psychcentral.com/news/2014/10/13/suicidal-thoughts-10-times-more-likely-in-adults-with-aspergers/76016.html And there's much more out there. It's really one of the worst things you can do to yourself. The last is particularly pertinent. Think about a person with psychosis and whether you could live the rest of your life like that, how most people would respond if asked that. What would cause a disorder to have a suicide rate even higher than that of psychosis? Humans weren't meant to be alone. In a way we're the most social, the most socially complex and cooperative, animals around, so much of our brain developed and is dedicated toward social behavior; when you take that away, everything can just fall apart. It also skews your perceptions and can lead to a self-reinforcing cycle. If you have a disorder that makes social interaction/relationships painful or difficult, get help, start working on it, as soon as possible. As flawed, inadequate, as other people and the world may seem, it can't be anywhere near as bad as how you can end up after isolating yourself to the extent I did. Even if you feel happy now, you really won't understand just how bad it can get until you have no one in your life, which can occur after you leave your parents and school, have a job with little or no meaningful social interaction; and if you've never experienced a healthy, fulfilling, social life/relationships, you really won't have a good reference point for how differently you could have felt. There are so many ways that relationships help develop yourself, so many things you can miss out on. There really are good people in the world you can find, even if you feel alienated, extremely uncommon, and have thought patterns, ideologies, that reinforce your negative view. Try to be more forgiving and explore seeing things in a new way. I'll share my own experience in the comments and answer any questions if anyone's interested. I essentially ended up as a hikikomori. I'm already trying to get as much help as I can, but it's probably going to be something that will haunt me and that I'll struggle with for the rest of my life, who knows how much permanent damage I may have done. An idea that's interested me is being the subject of a university study/research into the effects of prolonged severe social isolation. I have no idea how to go about this or whether anyone would be interested, if it's redundant and already been done before.
Toward a Neurology of Loneliness - The neurological effects of prolonged social isolation (x-post r/nootropics)
Cross posting to this to multiple relevant subreddits and sites because after much research, analysis, introspection, and rumination I came to realize that it's really been on the proximal cause of the majority of problems in my life and my suffering. This is a key factor that many factor that many may be resistant to accept, like I was, and that many are unaware of just how significant it is. Those interested in related fields may also find this fascinating. The comments in the original thread are worth reading: https://www.np.reddit.com/Nootropics/comments/3u999j/toward_a_neurology_of_loneliness_the_neurological Found this while researching the neurological effects of chronic (severe) social isolation. It's the most thorough overview I've found and demonstrates in horrifying detail how it's really one of the worst things you can do to yourself. https://cacioppo.squarespace.com/s/toward-a-neurology-of-loneliness-916y.pdf Not as long as it seems, 20 pages are tables on animal studies and the effects of depression found, along with a long reference section. Page 2 in particular has a good overview of the changes that occur in a socially isolated brain. Some key excerpts demonstrating just how bad the effects are compared to other detrimental factors, references removed to reduced size: In 2010, a meta-analysis revealed that the odds ratio for increased mortality for loneliness is 1.45, which is approximately double the odds ratio for increased mortality for obesity and quadruple the odds ratio for air pollution
Results showed that loneliness was associated with increased mortality risk over a 6-year period and that neither health behaviors nor objective features of social relationships (e.g., marital status, proximity to friends or family) could explain the association between loneliness and mortality. Several studies also indicate that loneliness is a risk factor for cognitive decline and dementia. For instance, Gow et al. (2007) investigated the correlates of changes in mental ability of 488 individuals from the Lothian Birth Cohort Study who were tested at ages 11 and 79. Among the variables tested were loneliness, social support, and objective social isolation, the last measured using a social network index (e.g., presence of significant others, number of significant others). After controlling for age, IQ, gender, years of education, and social class, only loneliness was associated significantly with changes in IQ. However, Gow et al. did not address the possibility that loneliness is a consequence rather than a predictor of cognitive decline.
Two recent longitudinal studies do speak to this question. Results at the 10-year follow-up assessment revealed that two biological measures and loneliness independently predicted cognitive decline. Cox proportional hazards models that controlled for age, sex, and education indicated that loneliness significantly increased the risk of clinical Alzheimer’s disease, and this association was unchanged when objective social isolation and other demographic and health-related factors were included as covariates.
Investigations designed to identify the mechanisms underlying the association between loneliness and mortality have found that loneliness is associated not only with increased risk for age-related cognitive decline and dementia but also with increased sleep fragmentation, increased hypothalamic pituitary adrenocortical (HPA) activity, altered gene expression indicative of decreased inflammatory control and increased glucocorticoid insensitivity, ), increased inflammation, elevated vascular resistance and blood pressure, higher rates of metabolic syndrome, and diminished immunity. Loneliness has also been associated with changes in psychological states that can contribute to morbidity and mortality, including increased depressive symptomatology, lower subjective wellbeing, , heightened vigilance for social threats, and decreased executive functioning.
A section on neurogenesis begins on page 29. Supporting articles: Maslow Be Damned: How Social Belonging Trumps Everything (Based on the work of Thomas Joiner, who has written what are possibly the best books on suicide) http://theviewfromhell.blogspot.com/2011/05/maslow-be-damned-how-social-belonging.html (A thread on this was posted before) One is the deadliest number: the detrimental effects of social isolation on cerebrovascular diseases and cognition. http://www.ncbi.nlm.nih.gov/pubmed/25537401 Suicidal Thoughts 10 Times More Likely in Adults With Asperger’s http://psychcentral.com/news/2014/10/13/suicidal-thoughts-10-times-more-likely-in-adults-with-aspergers/76016.html And there's much more out there. It's really one of the worst things you can do to yourself. The last is particularly pertinent. Think about a person with psychosis and whether you could live the rest of your life like that, how most people would respond if asked that. What would cause a disorder to have a suicide rate even higher than that of psychosis? Humans weren't meant to be alone. In a way we're the most social, the most socially complex and cooperative, animals around, so much of our brain developed and is dedicated toward social behavior; when you take that away, everything can just fall apart. It also skews your perceptions and can lead to a self-reinforcing cycle. If you have a disorder that makes social interaction/relationships painful or difficult, get help, start working on it, as soon as possible. As flawed, inadequate, as other people and the world may seem, it can't be anywhere near as bad as how you can end up after isolating yourself to the extent I did. Even if you feel happy now, you really won't understand just how bad it can get until you have no one in your life, which can occur after you leave your parents and school, have a job with little or no meaningful social interaction; and if you've never experienced a healthy, fulfilling, social life/relationships, you really won't have a good reference point for how differently you could have felt. There are so many ways that relationships help develop yourself, so many things you can miss out on. There really are good people in the world you can find, even if you feel alienated, extremely uncommon, and have thought patterns, ideologies, that reinforce your negative view. Try to be more forgiving and explore seeing things in a new way. I'll share my own experience in the comments and answer any questions if anyone's interested. I essentially ended up as a hikikomori. I'm already trying to get as much help as I can, but it's probably going to be something that will haunt me and that I'll struggle with for the rest of my life, who knows how much permanent damage I may have done. An idea that's interested me is being the subject of a university study/research into the effects of prolonged severe social isolation. I have no idea how to go about this or whether anyone would be interested, if it's redundant and already been done before.
Need some help with a reviewer question on residual categories and what I did with my missing data.
In a study I wrote together with some co-authors we performed bivariate and multivariate analyses using Cox proportional hazards models to estimate hazard ratios. Basically, we were trying to assess if there was a higher or lower risk of longer durations of a certain condition in year B compared to year A. We also performed a Kaplan-Meier survival estimate (we got survival curves for both years etc). Certain variables had been dropped before that when they had a high proportion of missing values. Also, when writing the Stata syntax for the Cox model and generating the event I made sure it only was created if the duration of the episode was not a missing value (Ie. replace event=1 if duration!=.). I used misschck (http://www.ats.ucla.edu/stat/stata/faq/nummiss_stata.htm) to see if the duration variable had any missing values and I got 0% missing values. That is all I know we did regarding missing values. Some secondary variables had a relatively high proportion of missing values but we kept them because they were interesting. Now I got a comment from a reviewer of the article asking how I treated the missing values in my regression analysis, and whether I had a residual category with missing values or I only used cases with full information (their point being that only using cases with full information would produce a selection bias). I honestly don't know what to answer to that, as I'm not familiar with residual categories. What should I answer to him? I know the first thing to do would be to ask my co-authors (I was only learning from them after all, and they should be able to guide me). But one of the co-authors is a bit of a difficult person to deal with, and although they were all supposed to guide me, I would rather try to deal with the situation instead of risking a lengthy internet argument. Thanks for the help in advance!
I am working with survival models and I am using R's coxph function to estimate the Cox proportional hazard model. To try it out, I am using the standard veteran dataset (obtainable by loading the "Survival" library and running data("veteran")). The regression looks like this:
coxph(formula = Surv(time, status) ~ ., data = veteran) coef exp(coef) se(coef) z p trt 0.22681 1.25459 0.18811 1.21 0.228 celltype 0.12969 1.13848 0.07765 1.67 0.095 karno -0.03533 0.96529 0.00540 -6.54 6.1e-11 diagtime 0.00216 1.00217 0.00907 0.24 0.811 age -0.00364 0.99637 0.00915 -0.40 0.691 prior -0.00784 0.99219 0.02228 -0.35 0.725 Likelihood ratio test=46 on 6 df, p=2.92e-08 n= 137, number of events= 128
I assume, since I did not specify anything special in the call to coxph, that this is learning a Cox proportional hazards model by maximizing the partial likelihood (which is what I want). The Wikipedia article on Cox proportional hazards models helpfully has the partial likelihood score function. At the maximum, the score should be zero (or at least near zero). I wanted to try this out to confirm that the maximum was found. I wrote some code in python to check:
def gradient(X, y, status, beta): # Initialize the value of the gradient. val = 0 # Iterate through the samples. for i in xrange(X.shape[0]): # For every uncensored example. if status[i] == 1: # The first part of the derivative. val += X[i] # The second part of the derivative. I initialize terms # beforehand to make computation easier. num, denom = 0, 0 # Iterate through all the other samples. for j in xrange(X.shape[0]): # If this record had longer observation time that the # current uncensored example. if y[j] >= y[i]: # Do derivative computations. denom += np.exp(X[j].dot(beta)) num += denom * X[j] # Ad append to the likelihood. val -= (num / denom) # Return the gradient (normalized by the number of uncensored events). return val / X.shape[0]
Running the gradient function with parameters close to
(0.22, 0.12, -0.35, 0.00216, -0.00364, -0.00784)
should give a gradient value close to zero. But it doesn't! It gives:
This is really not close to the zero vector. Hence I think I must have implemented the gradient incorrectly, but I can't find my mistake. Can anyone help?
Need assistance in understanding survival analysis and R coxph() output
I'm a grad student looking at the effects of gene expression on breast cancer patient survival. I'm proficient with R and doing a few types of data analysis, but I'm largely teaching myself survival analysis as I go. I've done univariate analysis with CoxPH and found a few genes to be significant. Now I'd like to do multivariate analysis and understand if multiple genes simultaneously have an effect on survival. I selected a few genes and here's what I've got: http://pastebin.com/mvg84Yq8 .
All but two of the covariates look like they give a significant contribution. What does it mean that some of them are significant but some are not? Will their significance vary depending on how many covariates I use and in what combination? Should I just toss out ones that aren't significant? What about adding more and more significant genes with stepAIC() or similar. Is there any benefit to this?
Should I just be looking at the likelihood/Wald/logrank test to see if the model is significant overall instead of individual p-values for each covariate? If the likelihood/Wald/logrank test has a small p-value, then is this saying that these covariates are having an affect on survival if the test p-value is low (even if all of the individual covariates aren't significant)?
The exp(coef) are all around 1. Does it mean anything that they are all so similar?
I've tried stratifying by a few clinical features (age, breast cancer stage, etc.). How are these strata reflected as I don't see them in the summary()? I do see two groups when I plot(survfit(mycoxph)).
Can I conclude that a stratification has an effect if without the strata() I get a no significance/high p-values while with the strata I get significance/low p-values? If I do use a strata, is there a way to get a p-value between their plots? What about a hazard ratio? I read something about having to use the means or keep certain covariates constant, but I didn't understand this.
Thank you so much for any guidance you can give me. I've been Googling trying to find tutorials and slides to explain this stuff to me all day.
Help with survival analysis of gene expression in R
I've tried reading up on some tutorials for doing survival analysis. Currently I'm looking at the effect of the expression of a particular gene on survival. Patients are separated into high and low expression groups based on mean expression time. I can do basic things like generate a Cox model on all patients and do a survfit() and plot my object/model. My questions are about how to perform the analysis of high vs low. Do I make a separate survival object, one for low and one for high, and then compare the two? Do I include all patients in one model and then strata() by my low and high groups? Once I have what I want to compare, how do I calculate the hazard ratio and associated p-values? Based on this link, do I just grab the coef's from my model and then divide one by the other? Do I always divide the high expression by low expression so that a ratio > 1 corresponds to increased hazard of having higher expression of the gene? Thank you for any guidance you can provide!
Das Cox-Modell (4) ist die populärste Regressionsmethode zur Analyse von Überlebensdaten. Es wird auch als proportionales Hazard Modell (engl.: proportional hazards model) bezeichnet. Ganz analog zu anderen Regressionsverfahren, wie der klassi-schen multiplen linearen Regression (3) oder der logistischen This function fits Cox's proportional hazards model for survival-time (time-to-event) outcomes on one or more predictors. Cox regression (or proportional hazards regression) is method for investigating the effect of several variables upon the time a specified event takes to happen. In the context of an outcome such as death this is known as Cox In a Cox proportional hazards regression model, the measure of effect is the hazard rate, which is the risk of failure (i.e., the risk or probability of suffering the event of interest), given that the participant has survived up to a specific time. A probability must lie in the range 0 to 1. Dieser Schätzer ist durch das Hazard Ratio gegeben. Voraussetzungen. Die Cox-Regression setzt voraus, dass das Hazard Ratio über die Zeit konstant ist (deshalb auch „proportional hazards odds ratio, and when by equating the two statistics we are sometimes forcing OR to be something it is not. Another statistic, which is often also perceived as a relative risk, is the hazard ratio (HR). We encounter it, for example, when we fit the Cox model to survival data. Under proportional hazards it is probably “natural” to think The Cox model can be written as a multiple linear regression of the logarithm of the hazard on the variables \(x_i\), with the baseline hazard being an ‘intercept’ term that varies with time. The quantities \(exp(b_i)\) are called hazard ratios (HR). The Hazard ratio (HR) is one of the measures that in clinical research are most often difficult to interpret for students and researchers. In this post we will try to explain this measure in terms of its practical use. You should know what the Hazard Ratio is, but we will repeat it again. Let’s take […] The Cox model can be written as a multiple linear regression of the logarithm of the hazard on the variables \(x_i\), with the baseline hazard being an ‘intercept’ term that varies with time. The quantities \(exp(b_i)\) are called hazard ratios (HR). Die Hazard Ratio (HR) errechnet sich mit 0,8. Eine Ratio von 0,8 bedeutet in diesem Beispiel, dass die Patienten der Gruppe II eine um 20% höhere Abheilungschance haben als die Patienten der Gruppe I. Bei einer HR von 1, besteht kein Unterschied zwischen den Gruppen. und 1.5 das tatsächliche Hazard Ratio für den Vergleich der Risi-ken für das Abstillen zwischen Müttern, die zum Zeitpunkt der Tab. 1 Ergebnisse des Cox-Modells für die Zeit bis zum Abstillen. Variable Hazard Ratio 95% Konfidenzintervall für das Hazard-Ratio p-Wert Wohlstand (hoch versus niedrig) 1.21 1.01 – 1.45 0.0417
About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators ... Explore how to fit a Cox proportional hazards model using Stata. We also describes how to check the proportional-hazards assumption statistically using -esta... Watch More: Statistics Course for Data Science https://bit.ly/2SQOxDH R Course for Beginners: https://bit.ly/1A1Pixc Getting Started with R using R St... A brief conceptual introduction to hazard ratios and survival curves (also known as Kaplan Meier plots). Hopefully this gives you the information you need to... To request the .pdf of the handout please contact us with the name of this presentation at:https://www.omegastatistics.com/contact/An overview of Kaplan-Meie... This video provides a demonstration of the use of Cox Proportional Hazards (regression) model based on example data provided in Luke & Homan (1998). A copy ... This video introduces Survival Analysis, and particularly focuses on explaining what the survival functions is, what the hazard is, and what the hazard ratio...
