Estimating Marginal Treatment Effects Under Compound Selection Bias in a Multistate Model Framework

Music City Center 

Estimating population-level treatment effects is challenging when participation involves sequential self-selection, such as screening compliance followed by treatment decisions. We propose a novel framework that integrates inverse probability weighting within a multistate model to address compound selection biases from these decision stages. Specifically, we first introduce an approach to correct for treatment-selection bias alone, then extend it to jointly correct bias from screening non-compliance and treatment choices. Our two-stage weighting approach accounts for systematic differences at both stages, enabling valid estimation of marginal treatment effects. In addition, compared to standard survival models, the multistate framework captures intermediate health states and competing risks, addressing the non-portability of relative risks and improving generalizability. Simulation studies show that ignoring multi-stage bias or using conventional survival models leads to substantial bias, while our method yields unbiased estimates. Application to the Kerala Oral Cancer Screening Trial illustrates the practical utility of the proposed approach.

Multistate models

Inverse probability weighting

Selection bias

Population-level treatment effects

Survival analysis

Causal inference