Evaluating the Proportional Hazards Assumption

2011 ◽  
pp. 161-200 ◽  
Author(s):  
David G. Kleinbaum ◽  
Mitchel Klein
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Assumption

Reporting of Multivariable Methods in the Medical Literature

2008 ◽  
Vol 56 (7) ◽  
pp. 954-957 ◽  
Author(s):  
Jeanette M. Tetrault ◽  
Maor Sauler ◽  
Carolyn K. Wells ◽  
John Concato
Keyword(s):  
Logistic Regression ◽  
New England ◽  
Proportional Hazards ◽  
Medical Literature ◽  
Multivariable Analysis ◽  
Cox Proportional Hazards ◽  
Primary Method ◽  
Primary Analysis ◽  
Proportional Hazards Assumption ◽  
Complete Reporting

BackgroundMultivariable models are frequently used in the medical literature, but many clinicians have limited training in these analytic methods. Our objective was to assess the prevalence of multivariable methods in medical literature, quantify reporting of methodological criteria applicable to most methods, and determine if assumptions specific to logistic regression or proportional hazards analysis were evaluated.MethodsWe examined all original articles in Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, and New England Journal of Medicine, from January through June 2006. Articles reporting multivariable methods underwent a comprehensive review; reporting of methodological criteria was based on each article's primary analysis.ResultsAmong 452 articles, 272 (60%) used multivariable analysis; logistic regression (89 [33%] of 272) and proportional hazards (76 [28%] of 272) were most prominent. Reporting of methodological criteria, when applicable, ranged from 5% (12/265) for assessing influential observations to 84% (222/265) for description of variable coding. Discussion of interpreting odds ratios occurred in 13% (12/89) of articles reporting logistic regression as the primary method and discussion of the proportional hazards assumption occurred in 21% (16/76) of articles using Cox proportional hazards as the primary method.ConclusionsMore complete reporting of multivariable analysis in the medical literature can improve understanding, interpretation, and perhaps application of these methods.

Comparison of survival distributions in clinical trials: A practical guidance

2020 ◽  
Vol 17 (5) ◽  
pp. 507-521
Author(s):  
Xiaotian Chen ◽  
Xin Wang ◽  
Kun Chen ◽  
Yeya Zheng ◽  
Richard J Chappell ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Proportional Hazards ◽  
Statistical Tests ◽  
Type I ◽  
Analysis Method ◽  
Primary Analysis ◽  
Logrank Test ◽  
Nonproportional Hazards ◽  
Advantages And Disadvantages ◽  
Proportional Hazards Assumption

Background In randomized clinical trials with censored time-to-event outcomes, the logrank test is known to have substantial statistical power under the proportional hazards assumption and is widely adopted as a tool to compare two survival distributions. However, the proportional hazards assumption is impossible to validate in practice until the data are unblinded. However, the statistical analysis plan of a randomized clinical trial and in particular its primary analysis method must be pre-specified before any unblinded information may be reviewed. Purpose The purpose of this article is to guide applied biostatisticians in the prespecification of a desired primary analysis method when a treatment effect with nonproportional hazards is anticipated. While articles proposing alternate statistical tests are aplenty, to the best of our knowledge, there is no article available that attempts to simplify the choice and prespecification of a primary statistical test under specific expected patterns on nonproportional hazards. We provide such guidance by reviewing various tests proposed as more powerful alternatives to the standard logrank test under nonproportional hazards and simultaneously comparing their performance under a wide variety of nonproportional hazards scenarios to elucidate their advantages and disadvantages. Method In order to select the most preferable test for detecting specific differences between survival distributions of interest while controlling false positive rates, we review and assess the performance of weighted and adaptively weighted logrank tests, weighted and adaptively weighted Kaplan–Meier tests and versatile tests under various patterns of nonproportional hazards treatment effects through simulation. Conclusion We validate some of the claimed properties of the proposed extensions and identify tests that may be more preferable under specific expected pattern of nonproportional hazards when such knowledge is available. We show that versatile tests, while achieving robustness to departures from proportional hazards, may lose interpretation of directionality (superiority or inferiority) and can only be seen to test departures from equality. Detailed summary and discussion of the performance of each test in terms of type I error rate and power are provided to formulate specific guidance about their applicability and use.

On D. Schoenfeld's approach for testing the proportional hazards assumption

Biometrika ◽  
1986 ◽  
Vol 73 (2) ◽  
pp. 513-515 ◽  
Author(s):  
T. MOREAU ◽  
J. O'QUIGLEY ◽  
J. LELLOUCH
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Assumption

scholarly journals Dietary Patterns May Be Nonproportional Hazards for the Incidence of Type 2 Diabetes: Evidence from Korean Adult Females

Nutrients ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2522
Author(s):  
Chung ◽  
Kim ◽  
Kwock
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Dietary Patterns ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Diabetes Incidence ◽  
Interaction Terms ◽  
Proportional Hazards Assumption

This study aimed to examine the association between the incidence of type 2 diabetes and various risk factors including dietary patterns based on the rigorous proportional hazards assumption tests. Data for 3335 female subjects aged 40–69 years from the Korea Genome and Epidemiology Study were used. The assumption of proportional hazards was tested using the scaled Schoenfeld test. The stratified Cox regression was used to adjust the nonproportionality of diabetic risk factors, and the regression was adjusted for potential confounding variables, such as age, marital status, physical activity, drinking, smoking, BMI, etc. Metabolic syndrome and meat and fish pattern variables were positively associated with diabetes. However, dietary patterns and metabolic syndrome variables violated the proportional hazards assumption; therefore, the stratified Cox regression with the interaction terms was applied to adjust the nonproportionality and to allow the possible different parameters over each stratum. The highest quartile of meat and fish pattern was associated with diabetes only in subjects aged over 60 years. Moreover, subjects who were obese and had metabolic syndrome had higher risk in bread and snacks (HR: 1.85; 95% CI: 1.00–3.40) and meat and fish pattern (HR: 1.82; 95% CI: 1.01–3.26), respectively. In conclusion, a quantitative proportional hazards assumption test should always be conducted before the use of Cox regression because nonproportionality of risk factors could induce limited effect on diabetes incidence.

scholarly journals Testing the proportional hazards assumption in case-cohort analysis

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiaonan Xue ◽  
Xianhong Xie ◽  
Marc Gunter ◽  
Thomas E Rohan ◽  
Sylvia Wassertheil-Smoller ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Cohort Analysis ◽  
Proportional Hazards Assumption
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