Statistical Models and Methods for Incomplete Data in Randomized Clinical Trials

Michael A. McIsaac () and Richard J. Cook ()
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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

Chapter Chapter 1 in Developments in Statistical Evaluation of Clinical Trials, 2014, pp 1-27 from Springer

Abstract: 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.

Keywords: Multiple Imputation; Last Observation Carry Forward; Miss Data Mechanism; Complete Data Likelihood; Empirical Standard Error (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-55345-5_1

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DOI: 10.1007/978-3-642-55345-5_1

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