Covariate adjustment increases statistical power in randomized controlled trials

2010 ◽  
Vol 63 (12) ◽  
pp. 1391 ◽  
Author(s):  
Hester Lingsma ◽  
Bob Roozenbeek ◽  
Ewout Steyerberg
Keyword(s):  
Randomized Controlled Trials ◽  
Statistical Power ◽  
Covariate Adjustment ◽  
Controlled Trials ◽  
Randomized Controlled

Statistical Power for Analyses of Changes in Randomized Controlled Trials

2005 ◽  
Vol 84 (3) ◽  
pp. 283-287 ◽  
Author(s):  
Y.-K. Tu ◽  
A. Blance ◽  
V. Clerehugh ◽  
M.S. Gilthorpe
Keyword(s):  
Randomized Controlled Trials ◽  
Statistical Methods ◽  
Statistical Power ◽  
Change Score ◽  
Error Rates ◽  
Analysis Of Covariance ◽  
Controlled Trials ◽  
Multivariate Statistical ◽  
Randomized Controlled ◽  
Study Power

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.

scholarly journals Statistical power considerations in genotype-based recall randomized controlled trials

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Naeimeh Atabaki-Pasdar ◽  
Mattias Ohlsson ◽  
Dmitry Shungin ◽  
Azra Kurbasic ◽  
Erik Ingelsson ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Statistical Power ◽  
Controlled Trials ◽  
Randomized Controlled
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