Research article summary (published 28 Aug 2009):

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

Full Abstract

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.

Author information

Author/s: Chen, Hua Yun (HY); Gao, Shasha (S);

Affiliation: Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA. hychen(-atsign-)uic.edu

Grants: R01 CA106355 (Agency:NCI NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-Aug; vol 28 (issue 19) : pp 2451-72

Dates: Created 2009/07/14; Completed 2009/10/01;

PMID: 19462416, status: MEDLINE (last retrieval date: 10/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Biometry Data Interpretation, Statistical Humans Likelihood Functions Linear Models

Linear Models Longitudinal Studies Models, Statistical Smoking Cessation - statistics & numerical data Treatment Outcome

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