The effects of non-compliance on intent-to-treat analysis of equivalence trials
Division of Biostatistics, Department of Environmental Medicine, New York University School of Medicine, New York, NY, USA.Statistics in Medicine (Impact Factor: 1.83). 04/2006; 25(7):1183-99. DOI: 10.1002/sim.2230
The standard approach for analysing a randomized clinical trial is based on intent-to-treat (ITT) where subjects are analysed according to their assigned treatment group regardless of actual adherence to the treatment protocol. For therapeutic equivalence trials, it is a common concern that an ITT analysis increases the chance of erroneously concluding equivalence. In this paper, we formally investigate the impact of non-compliance on an ITT analysis of equivalence trials with a binary outcome. We assume 'all-or-none' compliance and independence between compliance and the outcome. Our results indicate that non-compliance does not always make it easier to demonstrate equivalence. The direction and magnitude of changes in the type I error rate and power of the study depend on the patterns of non-compliance, event probabilities, the margin of equivalence and other factors.
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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 compliance behaviour in the dropout exists. We recommend that our approach be used in conjunction with methods of analysis which assume MAR in estimating the ITT effect.Statistics in Medicine 04/2008; 27(8):1164-79. DOI:10.1002/sim.3025 · 1.83 Impact Factor
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