Date of this Version
Statistics in Medicine
We present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent-to-treat longitudinal analyses based on random effects logistic models that are commonly used in primary care depression research. Our estimation approach is an extension of Nagelkerke et al.'s instrumental variables approximation for cross-sectional binary outcomes. Our approach is easily implementable with standard random effects logistic regression software. We show through a simulation study that our approach provides reasonably accurate inferences for the setting of the depression trial under model assumptions. We also evaluate the sensitivity of our approach to model assumptions for the depression trial.
This is the peer reviewed version of the following article: Small, D. S., Ten Have, T. R., Joffe, M. M. and Cheng, J. (2006), Random Effects Logistic Models for Analyzing Efficacy of a Longitudinal Randomized Treatment With Non-Adherence. Statist. Med., 25: 1981–2007., which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.2313/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms.
random effects, logistic regression, exclusion restriction, encouragement studies, mental health
Small, D. S., Ten Have, T. R., Joffe, M. M., & Cheng, J. (2006). Random Effects Logistic Models for Analyzing Efficacy of a Longitudinal Randomized Treatment With Non-Adherence. Statistics in Medicine, 25 (12), 1981-2007. http://dx.doi.org/10.1002/sim.2313
Date Posted: 27 November 2017
This document has been peer reviewed.