title: Do multiple outcome measures require p-value adjustment?
abstract: BACKGROUND
  Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used.
  
  
  CONCLUSIONS
  The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size.
  
  
  CONCLUSIONS
  Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.
authors.affiliation: Institute of Evidence-Based Chiropractic 6252 Rookery Road, Fort Collins, Colorado 80528, USA. rjf@chiroevidence.com
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Feise
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D016896:Treatment Outcome