The application of REML in clinical trials
- 30 August 1994
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 13 (16) , 1601-1617
- https://doi.org/10.1002/sim.4780131602
Abstract
Residual maximum likelihood (REML) is a technique for estimating variance components in multi-classified data. In contrast to analysis of variance it can be routinely applied to unbalanced data and avoids some of the problems of biased variance estimates found with standard maximum likelihood estimation. The full REML method is of particular value for the analysis of unbalanced clinical trials as it allows recovery of all the available information on treatment effects which can lead to significant improvements in their precision. The use of REML has until recently been limited by heavy computational requirements and lack of readily available software. This is no longer such a restriction, however, as REML procedures are now available in several widely-used statistical packages, including BMDP, Genstat and SAS. This paper describes the REML technique and discusses its application to three common types of clinical trial: crossover, repeated measures and multicentre.Keywords
This publication has 40 references indexed in Scilit:
- Can meta-analyses be trusted?The Lancet, 1991
- Analysis of data from multiclinic trialsControlled Clinical Trials, 1986
- Meta-analysis in clinical trialsControlled Clinical Trials, 1986
- Beta blockade during and after myocardial infarction: An overview of the randomized trialsProgress in Cardiovascular Diseases, 1985
- Yield variability of crop varieties in the U.K.The Journal of Agricultural Science, 1984
- The Estimation of Heritability with Unbalanced Data: I. Observations Available on Parents and OffspringBiometrics, 1977
- Maximum Likelihood Approaches to Variance Component Estimation and to Related ProblemsJournal of the American Statistical Association, 1977
- Estimation of Variance and Covariance Components in Linear ModelsJournal of the American Statistical Association, 1972