Assessing treatment effect through compliance score in randomized trials with noncompliance
Zonghui Hu, Zhiwei Zhang, Dean Follmann
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 4
Abstract
A randomized trial is the gold standard for assessing the benefit of a treatment versus a control. When noncompliance is present, treatment effect depends on the tendency to comply—an attribute that is not directly measurable. Though the principal causal effect has been the most important for handling noncompliance, it is not immediately applicable to clinical decision-making as it targets the average effect in the latent strata of potential compliance. In this work, we propose the concept of compliance score, a linear combination of baseline characteristics, that uncovers the inherent attribute of compliance. We then assess the heterogeneous causal effect, namely, the causal effect of treatment as a function of baseline characteristics through the compliance score. A pseudo-response, along with a nonparametric estimation procedure, is proposed to ensure consistent and optimally efficient estimation. Compare to principal causal effect, the proposed effect is actionable and allows prediction of treatment effect at individual level. This work is motivated by and applied to a clinical trial to evaluate the benefit of antiretroviral regimens in HIV-infected patients.
Keywords
Causal inference, noncompliance, Nonparametric regression, principal causal effect, principal stratification causal effect, randomized trial
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