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

Shahriar Noroozizadeh, Pim Welle, Jeremy Weiss, George H. Chen
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:573-609, 2025.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v287-noroozizadeh25a, title = {The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis}, author = {Noroozizadeh, Shahriar and Welle, Pim and Weiss, Jeremy and Chen, George H.}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {573--609}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/noroozizadeh25a/noroozizadeh25a.pdf}, url = {https://proceedings.mlr.press/v287/noroozizadeh25a.html}, 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.} }
Endnote
%0 Conference Paper %T The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis %A Shahriar Noroozizadeh %A Pim Welle %A Jeremy Weiss %A George H. Chen %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-noroozizadeh25a %I PMLR %P 573--609 %U https://proceedings.mlr.press/v287/noroozizadeh25a.html %V 287 %X 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.
APA
Noroozizadeh, S., Welle, P., Weiss, J. & Chen, G.H.. (2025). The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:573-609 Available from https://proceedings.mlr.press/v287/noroozizadeh25a.html.

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