Testing for center effects on survival and competing risks outcomes using pseudo-value regression. Lifetime Data Anal 2019 Apr;25(2):206-228
Date
07/07/2018Pubmed ID
29978275Pubmed Central ID
PMC6320737DOI
10.1007/s10985-018-9443-6Scopus ID
2-s2.0-85049594054 (requires institutional sign-in at Scopus site) 1 CitationAbstract
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 BRAuthor
Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Cluster AnalysisComputer Simulation
Data Accuracy
Data Interpretation, Statistical
Humans
Models, Statistical
Multicenter Studies as Topic
Regression Analysis
Reproducibility of Results
Risk Assessment
Survival Analysis