Premium
Estimation of average treatment effect with incompletely observed longitudinal data: Application to a smoking cessation study
Author(s) -
Chen Hua Yun,
Gao Shasha
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3617
Subject(s) - covariate , missing data , inference , outcome (game theory) , statistics , smoking cessation , sensitivity (control systems) , mixed model , estimating equations , estimation , econometrics , causal inference , mathematics , maximum likelihood , generalized estimating equation , computer science , medicine , artificial intelligence , management , mathematical economics , pathology , electronic engineering , engineering , economics
We study the problem of estimation and inference on the average treatment effect in a smoking cessation trial where an outcome and some auxiliary information were measured longitudinally, and both were subject to missing values. Dynamic generalized linear mixed effects models linking the outcome, the auxiliary information, and the covariates are proposed. The maximum likelihood approach is applied to the estimation and inference on the model parameters. The average treatment effect is estimated by the G‐computation approach, and the sensitivity of the treatment effect estimate to the nonignorable missing data mechanisms is investigated through the local sensitivity analysis approach. The proposed approach can handle missing data that form arbitrary missing patterns over time. We applied the proposed method to the analysis of the smoking cessation trial. Copyright © 2009 John Wiley & Sons, Ltd.