Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data

• Published in: Journal of the American Statistical Association Vol. 90; no. 429; pp. 106 - 121
• Main Authors: Robins, James M., Rotnitzky, Andrea, Zhao, Lue Ping
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis Group 01.03.1995; American Statistical Association; Taylor & Francis Ltd
• Subjects: AIDS; Algorithms; Censorship; Consistent estimators; Counterfactual causality; Dependent censoring; Economic models; Estimators; Exact sciences and technology; G-computation algorithm; Generalized estimating equations; Intent to treat; Leukocytes; Linear inference, regression; Logistics; Longitudinal data; Mathematics; Medical research; Missing data; Modeling; Noncompliance; Parametric models; Probability and statistics; Randomized trial; Regression analysis; Sciences and techniques of general use; Statistical variance; Statistics; Surrogate markers; Theory and Methods; Time-dependent covariates; Infection; Missing data; Randomization; Censoring; Follow up study; Regression model; Viral disease; Clinical trial; AIDS; Semiparametric model
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.1995.10476493

We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random. These estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable. The likelihood-based parametric G-computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance. But because of possible model misspecification, the parametric G-computation algorithm estimator, in contrast with the proposed weighted estimators, may be inconsistent for the difference in treatment-arm-specific means, even when compliance is completely at random and censoring is independent. We illustrate our methods with the analysis of the effect of zidovudine (AZT) treatment on the evolution of mean CD4 count with data from an AIDS clinical trial.