Sensitivity analysis of intention-to-treat estimates when withdrawals are related to unobserved compliance status

Abstract

In the presence of dropout, intent(ion)-to-treat analysis is usually carried out using methods that assume a missing-at-random (MAR) dropout mechanism. We investigate the potential bias caused by assuming MAR when the dropout is related to unobserved compliance status. A framework to assess the magnitude of bias in the context of pre- and post-test design (PPD) with two treatment arms is presented. Scenarios with all-or-none and partial compliance level are investigated. Using two simulated data sets and actual data from an e-mental health trial, we demonstrate the utility of sensitivity analyses to assess the bias magnitude and show that they are plausible options when some knowledge of com..