Analysis of Repeated Measures in the Presence of Missing Observations

• Activity Number: 336
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jing Jerry Li*+
• Companies: Merck
• Keywords: Missing data ; Repeated measures ; Missing at random ; Inverse probability weighting ; Generalized estimating equations ; Weighted generalized estimating equations

Abstract

Incomplete data is common in both observational studies and clinical trials. Ignoring missing data may produce seriously biased estimators and could lead to misleading results. Longitudinal and crossover studies with repeated measures are particularly subject to missing observations. Various methods, including generalized estimating equations (GEE), weighted GEE (WGEE) and multiple imputations, have been proposed to cope with missing data in longitudinal studies. However, very few researchers have explored the missing data issue in crossover studies. In addition to reviewing and critiquing the methods dealing with missing observations in general and in repeated measures, in this report, we propose a new weighting approach for GEE to estimate the regression parameters in crossover studies. The proposed method provides consistent and asymptotically normally distributed estimators. Simulation and asymptotic efficiency results indicate that the proposed estimators are more efficient than both regular GEE and WGEE.