Re-examining and calibrating weighted survival analysis for causal inference

2026-05-15

AI Summary

This study addresses the limitations of existing weighted survival analysis methods in causal inference, which often suffer from unclear statistical properties and inadequate confidence interval coverage. The authors integrate the weighted Kaplan–Meier estimator into the augmented inverse probability weighting (AIPW) framework, establishing—for the first time—a systematic theoretical connection between the two. Building on this foundation, they propose novel calibration-based approaches for both low- and high-dimensional survival analysis that combine the weighted Kaplan–Meier estimator, the Breslow–Peto estimator, and covariate balancing techniques. Extensive simulations and real-data analyses demonstrate that the proposed calibration method substantially improves confidence interval coverage while simultaneously yielding shorter intervals, achieving a favorable balance between robustness and statistical efficiency.

Abstract

Causal inference with time-to-event outcomes is fundamental in various scientific studies. In a static setup with fitted propensity scores, weighted Kaplan-Meier estimation for survival probabilities and weighted Breslow-Peto estimation for hazard ratios have been widely used, but their statistical properties have been overlooked or studied only to a limited extent. We re-examine the weighted Kaplan-Meier method by formally linking it with the general framework of augmented inverse probability weighted estimation including both point and variance estimation. Furthermore, to address limitations of existing weighted methods for survival analysis, we develop new methods and associated theory through calibrated estimation in both low-dimensional and high-dimensional settings. We present a simulation study and an empirical application on the effectiveness of adjunctive psychotropic treatments for patients with schizophrenia. The calibrated methods yield coverage proportions closer to target ones in the simulation study, and produce shorter confidence intervals in both simulation and empirical studies.

Problem

• causal inference
• survival analysis
• weighted estimation
• calibration
• time-to-event outcomes

Innovation

• calibrated estimation
• weighted survival analysis
• augmented inverse probability weighting
• high-dimensional inference
• causal inference