Ranking tailoring variables for constructing individualized treatment rules: An application to schizophrenia

Jiacheng Wu, Nina Galanter, Susan M. Shortreed and Erica E.M. Moodie

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 2, 309-330

Abstract: As with many chronic conditions, matching patients with schizophrenia to the best treatment option is difficult. Selecting antipsychotic medication is especially challenging because many of the medications can have burdensome side effects. Adjusting or tailoring medications based on patients' characteristics could improve symptoms. However, it is often not known which patient characteristics are most helpful for informing treatment selection. In this paper, we address the challenge of identifying and ranking important variables for tailoring treatment decisions. We consider a value‐search approach implemented through dynamic marginal structural models to estimate an optimal individualized treatment rule. We apply our methodology to the Clinical Antipsychotics Trial of Intervention and Effectiveness (CATIE) study for schizophrenia, to evaluate if some tailoring variables have greater potential than others for selecting treatments for patients with schizophrenia (Stroup et al., 2003, Schizophrenia Bulletin, 29, 15–31).

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:71:y:2022:i:2:p:309-330

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