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Testing for center effects on survival and competing risks outcomes using pseudo-value regression. Lifetime Data Anal 2019 Apr;25(2):206-228 PMID: 29978275 PMCID: PMC6320737

Pubmed ID





In multi-center studies, the presence of a cluster effect leads to correlation among outcomes within a center and requires different techniques to handle such correlation. Testing for a cluster effect can serve as a pre-screening step to help guide the researcher towards the appropriate analysis. With time to event data, score tests have been proposed which test for the presence of a center effect on the hazard function. However, sometimes researchers are interested in directly modeling other quantities such as survival probabilities or cumulative incidence at a fixed time. We propose a test for the presence of a center effect acting directly on the quantity of interest using pseudo-value regression, and derive the asymptotic properties of our proposed test statistic. We examine the performance of our proposed test through simulation studies in both survival and competing risks settings. The proposed test may be more powerful than tests based on the hazard function in settings where the center effect is time-varying. We illustrate the test using a multicenter registry study of survival and competing risks outcomes after hematopoietic cell transplantation.

Author List

Wang Y, Logan BR


Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of Wisconsin


jenkins-FCD Prod-332 f92a19b0ec5e8e1eff783fac390ec127e367c2b5