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Doubly-Robust Estimator of the Difference in Restricted Mean Times Lost with Competing Risks Data (309869)

Keywords: Restricted Mean Times Lost, Competing Risks, Pseudo-Observations, Inverse-Probability-Weighting, Fine-Gray Model, Doubly-Robust Estimator

In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is well specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We apply the proposed method to assess biomarker-treatment interaction in the POPLAR and OAK randomized trials of second-line therapy for advanced non-small-cell lung cancer.