Causal models for randomized physician encouragement trials in treating primary care depression

• 成果类型: Article
• 署名作者: Ten Have, TR; Elliott, MR; Joffe, M; Zanutto, E; Datto, C
• 署名单位: University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000034
• 发表日期: 2004
• 页码: 16-25
• 关键词: principal stratification; inference; identification

摘要

This article addresses unique causal issues in the context of a randomized study on improving adherence to best practice guidelines by primary care physicians (PCP's) in treating their depressed patients. The study assessed an encouragement strategy to improve PCP guideline adherence. In this context. we compare two causal approaches: the conditional-compliance (CC) Bayesian latent class and the conditional-observable (CO) structural mean model methods. The CC methods estimate contrasts between randomized encouragement and no-encouragement arms [intent-to-treat (ITT) estimand] given latent PCP guideline complier classes. The CO methods estimate contrasts between PCP guideline adherence and nonadherence conditions (as-treated estimand) given observed PCP adherence status. The CC ITT estimand for patients with PCP compliers equals the CO as-treated estimand depending on assumptions. One such assumption pertains to the absence of physician defiers, who do the opposite of what they are encouraged to do in treating patients for depression. We relate these two estimands to each other in our clinical context when the no-defier assumption is not plausible. In other contexts, previous statistical literature has appropriately assumed the absence of defiers. However, indications in the behavioral literature, anecdotal evidence in the study, and results from the data analysis and simulations suggest that defers do exist in the context of physician-based interventions in primary care. Both simulation and empirical results show that even with a small estimated proportion of defiers under Bayesian model assumptions, inference is sensitive to different assumptions about this class of PCP noncompliers.