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One of the most common ways researchers compare survival outcomes acrosstreatments when confounding is present is using Cox regression. This model islimited by its underlying assumption of proportional hazards; in some cases,substantial violations may occur. Here we present and compare approaches whichattempt to address this issue, including Cox models with time-varying hazardratios; parametric accelerated failure time models; Kaplan-Meier curves; andpseudo-observations. To adjust for differences between treatment groups, we useInverse Probability of Treatment Weighting based on the propensity score. Weexamine clinically meaningful outcome measures that can be computed anddirectly compared across each method, namely, survival probability at time T,median survival, and restricted mean survival. We conduct simulation studiesunder a range of scenarios, and determine the biases, coverages, and standarderrors of the Average Treatment Effects for each method. We then apply theseapproaches to two published observational studies of survival after cancertreatment. The first examines chemotherapy in sarcoma, where survival is verysimilar initially, but after two years the chemotherapy group shows a benefit.The other study is a comparison of surgical techniques for kidney cancer, wheresurvival differences are attenuated over time.

Analysis of survival data with non-proportional hazards: A comparison of propensity score weighted methods