Statistical Power for Analyses of Changes in Randomized Controlled Trials

Abstract

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.

Get full access to this article

View all access options for this article.

References

Blomqvist N, Dahlén G (1985). Analysis of change—are base-line measurements needed? Some statistical comments on common experimental design. J Clin Periodontol 12:877–881.

Bonate P (2000). Analysis of pretest-postest designs. Boca Raton, FL: Chapman & Hall/CRC.

Chow SC, Shao J (2002). A note on statistical methods for assessing therapeutic equivalence. Control Clin Trial 23:515–520.

Dawson B, Trapp RG (2001). Basic and clinical biostatistics. 3rd ed. New York: McGraw-Hill.

Esposito M, Coulthard P, Worthington HV (2003). Enamel matrix derivative (Emdogain^®) for periodontal tissue regeneration in intrabony defects (Cochrane Review). In: The Cochrane Library, Issue 2. http://www.thecochranelibrary.com

Everitt B (1995). The analysis of repeat measures: a practical review with examples. Statistician 44:113–135.

Fleiss JL (1992). General design issues in efficacy, equivalency and superiority trials. J Periodontal Res 27:306–313.

Greenstein G, Lamster I (2000). Efficacy of periodontal therapy: statistical versus clinical significance. J Periodontol 71:657–662.

Gunsolley JC, Elswick RK, Davenport JM (1998). Equivalence and superiority testing in regeneration clinical trials. J Periodontol 69:521–527.

10.

Hujoel PP, Baab DA, DeRouen TA (1992). The power of tests to detect differences between periodontal treatments in published studies. J Clin Periodontol 19:779–784.

11.

Koch GG, Paquette DW (1997). Design principles and statistical considerations in periodontal clinical trials. Ann Periodontol 2:42–63.

12.

Laster LL (1992). Some aspects of efficient experimental design and analysis in periodontal trials. J Periodontal Res 27:405–411.

13.

Lehnhoff RW, Grainger RM (1974). Use of analysis of covariance in periodontal clinical trials. J Periodontal Res 9(Suppl 14):143–159.

14.

Moher D, Schulz KF, Altman DG (2001). The CONSORT statement: revised recommendations for improving the quality of reports of parallel group randomized trials. BMC Med Res Methodol 1:2. http://biomedcentral.com

15.

Montenegro R, Needleman I, Moles D, Tonetti M (2002). Quality of RCTs in periodontology—a systematic review. J Dent Res 81:866–870.

16.

Moye LA (2000). Statistical reasoning in medicine: the intuitive P-value primer. New York: Springer-Verlag.

17.

Needleman IG, Giedrys-Leepper E, Tucker RJ, Worthington HV (2001). Guided tissue regeneration for periodontal infra-bony defects. Cochrane Data Base Systematic Review. J Periodontal Res 37:380–388.

18.

R Development Core Team (2003). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-00-3, URL http://www.R-project.org.

19.

Rethman MP, Nunn ME (1999). Clinical versus statistical significance. J Periodontol 70:700–702.

20.

Senn S, Stevens L, Chaturvedi N (2000). Repeated measures in clinical trials: simple strategies for analysis using summary measures. Stat Med 19:861–877.

21.

Tu YK, Maddick IH, Griffiths GS, Gilthorpe MS (2004). Mathematical coupling can undermine the statistical assessment of clinical research: illustration from the treatment of guided tissue regeneration. J Dent 32:133–142. Erratum J Dent 32:339–340, 2004.

22.

Vickers AJ (2001). The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Med Res Methodol 1:6. http://biomedcentral.com

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.35 MB