Within Person Randomized Trials

Gui-Shuang Ying ()
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Gui-Shuang Ying: University of Pennsylvania, Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine

Chapter 72 in Principles and Practice of Clinical Trials, 2022, pp 1377-1397 from Springer

Abstract: Abstract Within person randomized trials (e.g., trials using within subject controls) are often employed for conditions that affect paired organs or two or more body sites of a person. In within person trials, the paired organs or body sites of a person receive two competing interventions either concurrently or sequentially, and the outcome measures are taken from each of paired organs or body sites. The within person design is a useful and efficient tool because comparisons between two interventions are made within the same person, thus removing the inter-person variability. Within person trials are most commonly conducted in ophthalmology, dentistry, and dermatology. However, within person trials pose some challenges including the possible bias from the carry across effect, the difficulty in recruitment subjects with bilateral disease of similar characteristics, and the limitation in generalization of the trial results. The within person correlations in outcome measures also complicate the sample size determination and statistical analyses of trial data from within person trials. This chapter describes the rationale and requirements for employing within person design, the considerations in designing within person trials in various disease specialty areas. The appropriate methods for sample size calculation and the statistical analysis for within person trials are also described. Real trials are used throughout the chapter to demonstrate the trial design considerations, sample size calculation, and statistical analysis of correlated data from within person trials.

Keywords: Within person trials; Within subject controls; Within person correlation; Inter-eye correlation; Paired design; Split-mouth design; Carry across effect (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_101

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