Data monitoring: A hypothesis-testing approach for treatment-outcome research

Traditional inferential statistics require that hypotheses be evaluated at only I sample size. That is, researchers must choose how many participants will be included in a study before conducting analyses; they are not allowe d to add data if initial results are not significant. This requirement forc es researchers to choose among including more participants than necessary, risking inconclusive results, or violating the requirement by adding partic ipants. This study presents a more flexible approach, called data monitorin g, that allows repeating an analysis as the sample increases. First, the co st of the uncorrected data monitoring that researchers sometimes do is esti mated. Second, the correction that is needed to allow data monitoring while holding an overall alpha at a desired level is estimated. Third, the power of data-monitoring is compared with traditional approaches. This study als o provides an example of the use of data monitoring. At least in some circu mstances, data monitoring can reduce Type II error or the number of partici pants needed without sacrificing Type I error.