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Confidence intervals for the effect of a prognostic factor after selection of an ‘optimal’ cutpoint
Author(s) -
Holländer Norbert,
Sauerbrei Willi,
Schumacher Martin
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1611
Subject(s) - confidence interval , statistics , resampling , statistic , selection (genetic algorithm) , variance (accounting) , test statistic , inflation (cosmology) , mathematics , computer science , statistical hypothesis testing , physics , accounting , artificial intelligence , theoretical physics , business
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. Copyright © 2004 John Wiley & Sons, Ltd.