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Statistical Models and Methods for Incomplete Data in Randomized Clinical Trials

In: Developments in Statistical Evaluation of Clinical Trials

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  • Michael A. McIsaac

    (Queen’s University, Department of Public Health Sciences)

  • Richard J. Cook

    (University of Waterloo, Department of Statistics and Actuarial Science)

Abstract

In this chapter we discuss several models by which missing data can arise in clinical trials. The likelihood function is used as a basis for discussing different missing data mechanisms for incomplete responses in short-term and longitudinal studies, as well as for missing covariates. We critically discuss common ad hoc strategies for dealing with incomplete data, such as complete-case analyses and naive methods of imputation, and we review more broadly appropriate approaches for dealing with incomplete data in terms of asymptotic and empirical frequency properties. These methods include the EM algorithm, multiple imputation, and inverse probability weighted estimating equations. Simulation studies are reported which demonstrate how to implement these procedures and examine performance empirically.

Suggested Citation

  • Michael A. McIsaac & Richard J. Cook, 2014. "Statistical Models and Methods for Incomplete Data in Randomized Clinical Trials," Springer Books, in: Kees van Montfort & Johan Oud & Wendimagegn Ghidey (ed.), Developments in Statistical Evaluation of Clinical Trials, edition 127, chapter 0, pages 1-27, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-55345-5_1
    DOI: 10.1007/978-3-642-55345-5_1
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