Looking back at prospective studies.
Gur's perspective article raises important points about analytic methods and the clinical inferences drawn from retrospective statistical analyses of prospective studies. Specifically, he associates three problems with the scientific methods of retrospective analyses: (1) using the parametric receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) as a performance measure, (2) using Bonferroni adjustments to account for multiple comparisons, and (3) failing to evaluate the variability of results across sites and observers. Gur demonstrates these problems with a case study: a recent paper analyzing the Digital Mammographic Imaging Screening Trial (DMIST) (1). The issues he raises are not specific to either retrospective study designs or secondary exploratory analyses of large studies but are important issues to consider in many design settings. I address each of these issues in the following and relate them to the information provided by DMIST papers.
Rutter
Carolyn M
CM
Center for Health Studies, Group Health Cooperative, Seattle, WA 98101, USA. rutter.c@ghc.org
D001943:Breast Neoplasms / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D011446:Prospective Studies; D012372:ROC Curve; D012189:Retrospective Studies; D013223:Statistics as Topic