Confidence intervals for the effect of a prognostic factor after selection of an 'optimal' cutpoint.
When investigating the effects of potential prognostic or risk factors that have been measured on a quantitative scale, values of these factors are often categorized into two groups. Sometimes an 'optimal' cutpoint is chosen that gives the best separation in terms of a two-sample test statistic. It is well known that this approach leads to a serious inflation of the type I error and to an overestimation of the effect of the prognostic or risk factor in absolute terms. In this paper, we illustrate that the resulting confidence intervals are similarly affected. We show that the application of a shrinkage procedure to correct for bias, together with bootstrap resampling for estimating the variance, yields confidence intervals for the effect of a potential prognostic or risk factor with the desired coverage.
Holländer
Norbert
N
Institute of Medical Biometry and Medical Informatics, University Hospital Freiburg, Stefan-Meier-Strasse 26, Freiburg D-79104, Germany. noh@fdm.uni-freiburg.de
Sauerbrei
Willi
W
Schumacher
Martin
M
D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology; D003198:Computer Simulation; D016001:Confidence Intervals; D004273:DNA, Neoplasm / Q000737:chemistry / Q000235:genetics; D003627:Data Interpretation, Statistical; D018572:Disease-Free Survival; D005260:Female; D005434:Flow Cytometry; D006801:Humans; D011237:Predictive Value of Tests; D011379:Prognosis; D016196:S Phase / Q000235:genetics
D004273:DNA, Neoplasm