Dealing with competing risks: testing covariates and calculating sample size Export

@article{citeulike:6071948, abstract = {It is universally agreed that Kaplan-Meier estimates overestimate the probability of the event of interest in the presence of competing risks. Kalbfleisch and Prentice recommend using the cumulative incidence as an estimate of the probability of an event of interest. However, there is no consensus on how to test the effect of a covariate in the presence of competing risks. Using simulations, this paper illustrates that the Cox proportional hazards model gives valid results when employed in testing the effect of a covariate on the hazard rate and when estimating the hazard ratio. A method to calculate the sample size for testing the effect of a covariate on outcome in the presence of competing risks is also provided. Copyright {\copyright} 2002 John Wiley \& Sons, Ltd.}, address = {Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Canada}, author = {Pintilie, Melania}, citeulike-article-id = {6071948}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.1271}, citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/99018842/ABSTRACT}, doi = {10.1002/sim.1271}, issn = {1097-0258}, journal = {Statistics in Medicine}, keywords = {competing-risk, power}, number = {22}, pages = {3317--3324}, posted-at = {2009-11-05 02:00:30}, priority = {2}, title = {Dealing with competing risks: testing covariates and calculating sample size}, url = {http://dx.doi.org/10.1002/sim.1271}, volume = {21}, year = {2002} }

TY - JOUR ID - citeulike:6071948 L3 - citeulike-article-id:6071948 N2 - It is universally agreed that Kaplan-Meier estimates overestimate the probability of the event of interest in the presence of competing risks. Kalbfleisch and Prentice recommend using the cumulative incidence as an estimate of the probability of an event of interest. However, there is no consensus on how to test the effect of a covariate in the presence of competing risks. Using simulations, this paper illustrates that the Cox proportional hazards model gives valid results when employed in testing the effect of a covariate on the hazard rate and when estimating the hazard ratio. A method to calculate the sample size for testing the effect of a covariate on outcome in the presence of competing risks is also provided. Copyright © 2002 John Wiley & Sons, Ltd. IS - 22 JF - Statistics in Medicine SN - 1097-0258 AD - Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Canada EP - 3324 TI - Dealing with competing risks: testing covariates and calculating sample size VL - 21 SP - 3317 KW - competing-risk KW - power AU - Pintilie, Melania PY - 2002/// UR - http://dx.doi.org/10.1002/sim.1271 ER -