Methods for non-proportional hazards in clinical trials: A systematic review.

Cox model log-rank test non-proportional hazards right-censored observations survival analysis

Journal

Statistical methods in medical research
ISSN: 1477-0334
Titre abrégé: Stat Methods Med Res
Pays: England
ID NLM: 9212457

Informations de publication

Date de publication:
09 Apr 2024
Historique:
medline: 9 4 2024
pubmed: 9 4 2024
entrez: 9 4 2024
Statut: aheadofprint

Résumé

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Identifiants

pubmed: 38592333
doi: 10.1177/09622802241242325
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

9622802241242325

Déclaration de conflit d'intérêts

Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Auteurs

Maximilian Bardo (M)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Maximilian Bardo and Cynthia Huber contributed equally to this study.

Cynthia Huber (C)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Maximilian Bardo and Cynthia Huber contributed equally to this study.

Norbert Benda (N)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Federal Institute for Drugs and Medical Devices, Bonn, Germany.

Jonas Brugger (J)

Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria.

Tobias Fellinger (T)

Agentur für Gesundheit und Ernährungssicherheit (AGES), Vienna, Austria.

Vaidotas Galaune (V)

Department of Pharmacy, Uppsala University, Uppsala, Sweden.

Judith Heinz (J)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Harald Heinzl (H)

Center for Medical Data Science, Section of Clinical Biometrics, Medical University of Vienna, Vienna, Austria.

Andrew C Hooker (AC)

Department of Pharmacy, Uppsala University, Uppsala, Sweden.

Florian Klinglmüller (F)

Agentur für Gesundheit und Ernährungssicherheit (AGES), Vienna, Austria.

Franz König (F)

Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria.

Tim Mathes (T)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Martina Mittlböck (M)

Center for Medical Data Science, Section of Clinical Biometrics, Medical University of Vienna, Vienna, Austria.

Martin Posch (M)

Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria.

Robin Ristl (R)

Center for Medical Data Science, Section of Medical Statistics, Medical University of Vienna, Vienna, Austria.

Tim Friede (T)

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Classifications MeSH