Analysis of Data with Missing Values
Missing data in longitudinal studies
Article first published online: 12 OCT 2006
Copyright © 1988 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 7, Issue 1-2, pages 305–315, January - February 1988
How to Cite
Laird, N. M. (1988), Missing data in longitudinal studies. Statist. Med., 7: 305–315. doi: 10.1002/sim.4780070131
- Issue published online: 12 OCT 2006
- Article first published online: 12 OCT 2006
- NIH. Grant Number: GM-29745
- Ignorable and non-ignorable non-response;
- Maximum likelihood;
- EM algorithm
When observations are made repeatedly over time on the same experimental units, unbalanced patterns of observations are a common occurrence. This complication makes standard analyses more difficult or inappropriate to implement, means loss of efficiency, and may introduce bias into the results as well. Some possible approaches to dealing with missing data include complete case analyses, univariate analyses with adjustments for variance estimates, two-step analyses, and likelihood based approaches. Likelihood approaches can be further categorized as to whether or not an explicit model is introduced for the nonresponse mechanism. This paper will review the use of likelihood based analyses for longitudinal data with missing responses, both from the point of view of ease of implementation and appropriateness in view of the non-response mechanism. Models for both measured and dichotomous outcome data will be discussed. The appropriateness of some non-likelihood based analyses is briefly considered.