Project Details
Projekt
Resampling-based inference for causal effect estimates in time-to-event data
Subject Area
Epidemiology and Medical Biometry/Statistics
Term
since 2020
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 439942859
Drawing causal conclusions from data becomes more and more relevant, not only in the context of growing data bases and therefore increasing use of observational data, but also in randomized controlled trials, where analyses often need to be adjusted for (despite randomization) unbalanced covariates or time-varying exposures arising from, e.g., a lack of adherence. The situation is even more complex, when the outcome is time-to-event, possibly subject to competing risks. During the first phase of the project, we rigorously investigated different resampling schemes for the estimation of confidence intervals and confidence bands of the average treatment effect in a setting with competing risks and right-censored data. It is the aim of this renewal proposal to extend the results in several directions: First, the issue of left-truncation has so far received little attention in causal inference literature. It is, however, relevant in several scenarios, reaching from pregnancy studies to time scales such as age or calendar time. Second, a common approach in causal inference is matching, e.g. based on propensity scores. Since this results in dependent data, the nonparametric bootstrap is no longer valid, but wild bootstrap approaches might still work, as has been shown in settings with a continuous or binary outcome, but not in the context of survival data, before. Third, a more complicated situation arises, when time-dependent confounding is present. We also aim to develop methods for this context, investigating landmark approaches and extending previous work on exposure density sampling and nested exposure case-control sampling. All methods will be available to a broad scientific audience by providing R code. This also increases both transparency and replicability of published simulation results. Moreover, simulated data shall be supplemented with empirical studies based on real data to increase the explanatory power of the results.
DFG Programme
Research Grants
