title: A simple method for calculating power based on a prior trial.
abstract: OBJECTIVE
  When an investigator wants to base the power of a planned clinical trial on the outcome of another trial, the latter study may not have been reported in sufficient detail to allow this. For example, when the outcome is a change from baseline, the power calculation requires the standard deviation of the difference, and it frequently happens that only the standard deviations of the baseline and the follow-up measurements are reported. Also when a complex analysis or an analysis with covariates is planned, the power calculation may be difficult or impossible. The objective was to develop a method to determine the power of a trial, based on minimal information from a previous (reference) trial.
  
  
  METHODS
  We investigated the power calculation for a range of statistical methods, including the t-test, analysis of covariance, analysis of variance, linear regression, logistic regression, Poisson regression, the Wilcoxon test, and the logrank test.
  
  
  RESULTS
  A method to calculate the power of a trial solely based on the P-value or the confidence interval of the outcome of the reference study.
  
  
  CONCLUSIONS
  A power calculation based on an earlier similar trial only requires its P-value.
authors.affiliation: Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Centre, Geert Grooteplein 21, Nijmegen, The Netherlands. g.borm@ebh.umcn.nl
authors.forename: George F
authors.initials: GF
authors.lastname: Borm
authors.forename: Bastiaan R
authors.initials: BR
authors.lastname: Bloem
authors.forename: Marten
authors.initials: M
authors.lastname: Munneke
authors.forename: Steven
authors.initials: S
authors.lastname: Teerenstra
mesh_terms: D000704:Analysis of Variance; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D018849:Controlled Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D011336:Probability; D012107:Research Design / Q000592:standards*; D018401:Sample Size