Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials


Beunckens, C; Molenberghs, G; Kenward, MG; (2005) Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials. Clinical trials (London, England), 2 (5). pp. 379-386. ISSN 1740-7745 DOI: https://doi.org/10.1191/1740774505cn119oa

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Abstract

Background In many clinical trials, data are collected longitudinally overtime. In such studies, missingness, in particular dropout, is an often encountered phenomenon. Methods We discuss commonly used but often problematic methods such as complete case analysis and last observation carried forward and contrast them with broadly valid and easy to implement direct-likelihood methods. We comment on alternatives such as multiple imputation and the expectation-maximization algorithm. Results We apply these methods in particular to data from a study with continuous outcomes. The outcomes are modelled using a general linear mixed-effects model. The bias with CC and LOCF is established in the case study and the advantages of the direct-likelihood approach shown. Conclusions We have established formal but easy to understand arguments for a shift towards a direct-likelihood paradigm when analysing incomplete data from longitudinal clinical trials, necessitating neither imputation nor deletion.

Item Type: Article
Keywords: Incomplete data, drop-outs, attitudes
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 16315646
Web of Science ID: 233178800001
URI: http://researchonline.lshtm.ac.uk/id/eprint/12435

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