Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non-adherence.
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.
Small
Dylan S
DS
Department of Statistics, Wharton School, University of Pennsylvania, 464 JMHH/6340, Philadelphia, PA 19104, USA. dsmall@wharton.upenn.edu
Ten Have
Thomas R
TR
Joffe
Marshall M
MM
Cheng
Jing
J
D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data