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Statistical Power for Analyses of Changes in Randomized Controlled Trials

¹ Department of Periodontology, Leeds Dental Institute, University of Leeds, Clarendon Way, Leeds, LS2 9LU, UK; and
² Biostatistics Unit, Centre for Epidemiology & Biostatistics, Leeds Institute of Genetics, Health & Therapeutics, University of Leeds, 30/32 Hyde Terrace, Leeds, LS2 9LN, UK;

^* corresponding author, y.k.tu{at}leeds.ac.uk

Randomized controlled trials (RCTs) are widely recommended as the most useful study design to generate reliable evidence and guidance to daily practices in medicine and dentistry. However, it is not well-known in dental research that different statistical methods of data analysis can yield substantial differences in study power. In this study, computer simulations are used to explore how using different univariate and multivariate statistical methods of analyzing change in continuous outcome variables affects study power, and the sample size required for RCTs. Results show that, in general, analysis of covariance (ANCOVA) yields greater power than other statistical methods in testing the superiority of one treatment over another, or in testing the equivalence between two treatments. Therefore, ANCOVA should be used in preference to change score or percentage change score to reduce type II error rates.

KEY WORDS: power • sample size • randomized controlled trials

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