Estimation of average treatment effect with incompletely observed longitudinal data: Application to a smoking cessation study

We stud the problem of estimation and inference oil 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 of 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 call handle missing data that form arbitrary missing patterns over little. We applied the proposed method to the analysis of the smoking cessation trial. Copyright (C) 2009 John Wiley & Sons. Ltd.