Analysis of survival data with nonproportional hazards: A comparison of propensity-score-weighted methods.

ABSTRACT: One of the most common ways researchers compare cancer survival outcomes across treatments from observational data is using Cox regression. This model depends on its underlying assumption of proportional hazards, but in some real-world cases, such as when comparing different classes of cancer therapies, substantial violations may occur. In this situation, researchers have several alternative methods to choose from, including Cox models with time-varying hazard ratios; parametric accelerated failure time models; Kaplan-Meier curves; and pseudo-observations. It is unclear which of these models are likely to perform best in practice. To fill this gap in the literature, we perform a neutral comparison study of candidate approaches. We examine clinically meaningful outcome measures that can be

SUBMITTER: Handorf EA 

PROVIDER: S-EPMC10282107 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature