ArticleStatistics in biopharmaceutical research2020
Effects of Proportional Hazard Assumption on Variable Selection Methods for Censored Data.
Article in Statistics in biopharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Association of Systolic and Diastolic Blood Pressure with the Risk of End-Stage Renal Disease in Older Type 2 Diabetes Mellitus Patients without Cardiovascular Disease: A Nationwide Population-Based Study (Diabetes Metab J 2025;49:1308-17).Diabetes & metabolism journal · 2026Article
- Comment on "Lean body mass index and hypertension risk in men: a nationwide epidemiological cohort study".Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Effects of Zibotentan and Dapagliflozin on Fluid Retention in Patients with CKD: Need More Evidence.Journal of the American Society of Nephrology : JASN · 2025Article
- Methodological Considerations Regarding the Article "Association Between Non-Benzodiazepine Hypnotics and Tinnitus: A Nationwide Cohort Study in Taiwan" [Letter].Nature and science of sleep · 2025Article
- Comment on 'Change in Physical Activity and Its Association With Decline in Kidney Function: A UK Biobank-Based Cohort Study' by Liu et al.Journal of cachexia, sarcopenia and muscle · 2024Article
- Limitations of the cox proportional hazards model and alternative approaches in metachronous recurrence research.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Association of Uterine Leiomyoma with Type 2 Diabetes Mellitus in Young Women: A Population-Based Cohort Study (Diabetes Metab J 2024;48:1105-13).Diabetes & metabolism journal · 2024Article
- Microbiome compositional data analysis for survival studies.NAR genomics and bioinformatics · 2024Article
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2 authors.
Funding
Abstract
The Cox proportional hazard (PH) model is widely used to determine the effects of risk factors and treatments (covariates) on survival time of subjects that might be right censored. The selection of covariates depends crucially on the specific form of the conditional hazard model, which is often assumed to be PH, Accelerated Failure time (AFT) or proportional odds (PO). However, we show that none of these semi-parametric models allow for the crossing of the survival functions and hence such strong assumptions may adversely affect the selection of variables. Moreover, the most commonly used PH assumption may also be violated when there is a delayed effect of the risk factors. Taking into account all of these modeling assumptions, this study examines the effect of the PH assumption on covariate selection when the data generating model may have non-PH. In particular, variable selection under two alternative models are explored: (i) the penalized PH model (using the elastic-net penalty) and (ii) the linear spline based hazard regression model. We apply the aforementioned models to the ACTG-175 data set and simulated data sets with survival times generated from the Weibull and log-normal distributions. We also examine the effect on covariate selection of stratifying the analysis on the off-treatment indicator.
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