Incomplete data in repeated measures analysis
- 1 November 1992
- journal article
- research article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 1 (3) , 275-295
- https://doi.org/10.1177/096228029200100304
Abstract
Complete (or balanced) repeated measures data arise when all subjects in a study are measured at the same set of time points. Data are often incomplete, because measurements are missed, or the design of the study results in subjects being measured at different sets of time points. This article reviews methods of analysis for incomplete repeated-measures data of this form, from an applied statistician's perspective. Limitations of approaches that (a) ignore between-subject variation, or (b) impute for missing values are discussed. Two methods are advocated that are relatively easy to implement using existing software, namely between-subject analysis of within-subject summary measures, and maximum likelihood based on a model for the data. Methods are applied and compared on four real-data examples with varied analytical objectives.Keywords
This publication has 35 references indexed in Scilit:
- Analysing changes in the presence of informative right censoring caused by death and withdrawalStatistics in Medicine, 1988
- Multiple Imputation for Nonresponse in SurveysPublished by Wiley ,1987
- Survey Nonresponse Adjustments for Estimates of MeansInternational Statistical Review, 1986
- Causal Models for Patterns of NonresponseJournal of the American Statistical Association, 1986
- Alternative Methods for CPS Income ImputationJournal of the American Statistical Association, 1986
- A Note About Models for Selectivity BiasEconometrica, 1985
- Imputation of Missing Values When the Probability of Response Depends on the Variable Being ImputedJournal of the American Statistical Association, 1982
- Models for Nonresponse in Sample SurveysJournal of the American Statistical Association, 1982
- Inference and missing dataBiometrika, 1976
- An Inventory for Measuring DepressionArchives of General Psychiatry, 1961