Estimate Optimal Dynamic Treatment Regimes with Partial Compatibility: An Application to data from a TREAT Asia HIV...

Dynamic treatment regimes (DTRs) formalize sequences of treatment decision rules that adapt to a patient's evolving clinical status. In observational studies, estimation of the value of a DTR (i.e., the expected population-outcome of a treatment strategy) using inverse probability weighting (IPW) and augmented IPW can be unstable when strict regime compatibility yields few valid participant trajectories, leading to highly variable weights and noisy estimates. In this paper, we introduce two adaptive weighting frameworks: generalized compatibility weighting (GCW) and bootstrap compatibility windowing (BCW), that allow near-compatible trajectories to contribute information in limited samples, with diminishing information-borrowing in larger samples. We illustrate how these estimators reduce noise in estimation and stabilize empirical optimal treatment threshold identification. Using data from adults living with HIV in one site of the TREAT Asia HIV Observational Database in India observed from 2013-2024, we evaluate clinically interpretable threshold-based decision rules based on baseline CD4 count and weight. In this application, GCW and BCW produced more stable estimated regime values than standard IPW-based approaches, with reduced variability across candidate thresholds and more reliable identification of empirically optimal treatment rules. These methods provide practical tools for improving treatment rule evaluation in observational studies.