title: Avoid lost discoveries, because of violations of standard assumptions, by using modern robust statistical methods.
abstract: OBJECTIVE
  Recently, there have been major advances in statistical techniques for assessing central tendency and measures of association. The practical utility of modern methods has been documented extensively in the statistics literature, but they remain underused and relatively unknown in clinical trials. Our objective was to address this issue. STUDY DESIGN AND PURPOSE: The first purpose was to review common problems associated with standard methodologies (low power, lack of control over type I errors, and incorrect assessments of the strength of the association). The second purpose was to summarize some modern methods that can be used to circumvent such problems. The third purpose was to illustrate the practical utility of modern robust methods using data from the Well Elderly 2 randomized controlled trial.
  
  
  RESULTS
  In multiple instances, robust methods uncovered differences among groups and associations among variables that were not detected by classic techniques. In particular, the results demonstrated that details of the nature and strength of the association were sometimes overlooked when using ordinary least squares regression and Pearson correlation.
  
  
  CONCLUSIONS
  Modern robust methods can make a practical difference in detecting and describing differences between groups and associations between variables. Such procedures should be applied more frequently when analyzing trial-based data.
authors.affiliation: Department of Psychology, University of Southern California, 618 Seeley Mudd Building, University Park Campus, Los Angeles, CA 90089-1061, USA. rwilcox@usc.edu
authors.forename: Rand
authors.initials: R
authors.lastname: Wilcox
authors.forename: Mike
authors.initials: M
authors.lastname: Carlson
authors.forename: Stan
authors.initials: S
authors.lastname: Azen
authors.forename: Florence
authors.initials: F
authors.lastname: Clark
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000592:standards; D012107:Research Design / Q000592:standards / Q000706:statistics & numerical data; D013223:Statistics as Topic / Q000379:methods / Q000592:standards; D016896:Treatment Outcome