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Statistical Analysis of Repeated-Measures Data with Drop-outs

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Clinical Trials in Neurology

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Abstract

Many clinical trials in neurology follow patients longitudinally over time, and measure clinical outcomes of interest repeatedly. A common problem that arises in this context is missing data caused by subjects dropping out prior to the end of the study. For earlier discussions of this topic, see Heyting et al. [1], Gornbein et al. [2], Little and Schenker [3] or Little [4]. This chapter reviews some approaches to statistical analysis when faced with this problem. I first discuss some simple approaches, including complete-case analyses that restrict attention to the cases that do not drop out. The latter are liable to yield biased answers when individuals that drop out differ systematically from those that remain in the study, as is often likely to be the case. I then discuss two more modern approaches that eliminate or reduce the bias of complete-case analysis by exploiting partial information on nonrespondents, namely maximum likelihood based on repeated-measures models, and multiple imputation. Methods are illustrated on an intent-to-treat analysis of data from a randomized clinical trial for tacrine in the treatment of Alzheimer’s disease, reported in detail in Little and Yau [5].

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Authors
  1. R. J. Little
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Editors and Affiliations

  1. Neuromuscular Unit, West London Neurosciences Centre, 1st Floor Pilot Wing, Charing Cross Hospital, Fulham Palace Road, W6 8RF, London, UK

    Roberto J. Guiloff MD, FRCP (Consultant Neurologist) (Consultant Neurologist)

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© 2001 Springer-Verlag London

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Little, R.J. (2001). Statistical Analysis of Repeated-Measures Data with Drop-outs. In: Guiloff, R.J. (eds) Clinical Trials in Neurology. Springer, London. https://doi.org/10.1007/978-1-4471-3787-0_13

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  • DOI: https://doi.org/10.1007/978-1-4471-3787-0_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-856-0

  • Online ISBN: 978-1-4471-3787-0

  • eBook Packages: Springer Book Archive

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