The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis

Published: 01 Jan 2025, Last Modified: 19 Aug 2025CHIL 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This study aims to quantify the association between non-adherence to antipsychotic medications and adverse outcomes among individuals with schizophrenia. We frame this problem in the context of survival analysis, looking at the time until the earliest of several types of adverse outcomes (early death, involuntary hospitalization, jail booking)–we refer to this time duration as the adverse event time. We apply standard causal inference tools (T-learner, S-learner, and nearest neighbor matching) with various survival models to estimate individual and average treatment effects in terms of differences in mean adverse event times, where the treatment corresponds to medication non-adherence. We repeat our analysis using different amounts of longitudinal information available per individual (3, 6, 9, and 12 months). Using real data from a county’s administrative records, our results show strong evidence that medication non-adherence is associated with earlier adverse outcomes, advancing the onset of an adverse event by approximately 1 to 4 months. Ablation studies confirm that risk scores provided by the county account for key confounders, as their removal amplifies the estimated effects of non-adherence. Finally, subgroup analyses by medication formulation (injectable vs. oral) and by specific medication type consistently show that non-adherence is associated with earlier adverse outcomes. These findings underscore the clinical importance of medication adherence in delaying severe psychiatric crises and show that integrating survival analysis with causal inference tools can yield policy-relevant insights in complex healthcare settings. We caution that although we use causal inference tools, we only make associative claims; we discuss the validity of some assumptions that would enable us to rigorously convert our claims into causal ones.
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