Statistics in Medicine

Estimates of clinically useful measures in competing risks survival analysis

Journal Article


Competing risks occur frequently in follow‐up clinical studies. To assess treatment or covariate effects, measures of clinical impact based on crude cumulative incidence should be considered, such as relative risks or the absolute risk reduction. In this work, transformation models through suitable link functions provide a straightforward approach to obtain point and interval estimates of such measures. An extension of the Klein and Andersen proposal, based on pseudo‐values, is considered. Non‐additive effects were tested by interactions between baseline (spline function on time) and covariates. The methods are applied to the evaluation of the impact of axillary lymph node nanometastases on metastatic relapse of breast cancer patients. Further, a literature data set on prostate cancer was used for illustration. Copyright © 2008 John Wiley & Sons, Ltd.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.