%0 Journal Article %1 Katsahian:2011:Stat-Med:21341296 %A Katsahian, S %A Boudreau, C %D 2011 %J Stat Med %K CompetingRisks SurvivalAnalysis oncology statistics %N 13 %P 1608-1617 %R 10.1002/sim.4132 %T Estimating and testing for center effects in competing risks %U https://www.ncbi.nlm.nih.gov/pubmed/21341296 %V 30 %X The problems of fitting Gaussian frailties proportional hazards models for the subdistribution of a competing risk and of testing for center effects are considered. In the analysis of competing risks data, Fine and Gray proposed a proportional hazards model for the subdistribution to directly assess the effects of covariates on the marginal failure probabilities of a given failure cause. Katsahianbiet al. extended their model to clustered time to event data, by including random center effects or frailties in the subdistribution hazard. We first introduce an alternate estimation procedure to the one proposed by Katsahian et al. This alternate estimation method is based on the penalized partial likelihood approach often used in fitting Gaussian frailty proportional hazards models in the standard survival analysis context, and has the advantage of using standard survival analysis software. Second, four hypothesis tests for the presence of center effects are given and compared via Monte-Carlo simulations. Statistical and numerical considerations lead us to formulate pragmatic guidelines as to which of the four tests is preferable. We also illustrate the proposed methodology with registry data from bone marrow transplantation for acute myeloid leukemia (AML).

@article{Katsahian:2011:Stat-Med:21341296, abstract = {The problems of fitting Gaussian frailties proportional hazards models for the subdistribution of a competing risk and of testing for center effects are considered. In the analysis of competing risks data, Fine and Gray proposed a proportional hazards model for the subdistribution to directly assess the effects of covariates on the marginal failure probabilities of a given failure cause. Katsahianbiet al. extended their model to clustered time to event data, by including random center effects or frailties in the subdistribution hazard. We first introduce an alternate estimation procedure to the one proposed by Katsahian et al. This alternate estimation method is based on the penalized partial likelihood approach often used in fitting Gaussian frailty proportional hazards models in the standard survival analysis context, and has the advantage of using standard survival analysis software. Second, four hypothesis tests for the presence of center effects are given and compared via Monte-Carlo simulations. Statistical and numerical considerations lead us to formulate pragmatic guidelines as to which of the four tests is preferable. We also illustrate the proposed methodology with registry data from bone marrow transplantation for acute myeloid leukemia (AML).}, added-at = {2019-11-18T10:19:03.000+0100}, author = {Katsahian, S and Boudreau, C}, biburl = {https://www.bibsonomy.org/bibtex/28b1b41ff566dae3a360e6bb3ad2c0849/jkd}, description = {Estimating and testing for center effects in competing risks. - PubMed - NCBI}, doi = {10.1002/sim.4132}, interhash = {f0e030ee65d68855df00289c90ad449f}, intrahash = {8b1b41ff566dae3a360e6bb3ad2c0849}, journal = {Stat Med}, keywords = {CompetingRisks SurvivalAnalysis oncology statistics}, month = jun, number = 13, pages = {1608-1617}, pmid = {21341296}, timestamp = {2019-11-18T10:19:03.000+0100}, title = {Estimating and testing for center effects in competing risks}, url = {https://www.ncbi.nlm.nih.gov/pubmed/21341296}, volume = 30, year = 2011 }