Document Detail


Nonparametric tests for continuous covariate effects with multistate survival data.
MedLine Citation:
PMID:  18266896     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In clinical trials and observational studies, it is often of scientific interest to evaluate the effects of covariates on complex multistate event probabilities. With discrete covariates, nonparametric tests may be constructed using estimates of the relevant quantities. With continuous covariates, a common approach is to arbitrarily discretize the covariates, which may lead to substantial information loss. Another strategy is to formulate the covariate effects in a regression model. Model-based tests may have either low power or be biased under misspecification. We propose nonparametric tests not requiring arbitrary discretization. The tests involve integrals of estimates continuously indexed by dichotomizations of the covariates. General asymptotic results are derived under null and alternative hypotheses, and verified using empirical process theory in several special cases. The tests are consistent under stochastic ordering, which arises naturally with multistate data. A novel nonparametric measure of covariate effect is studied as a natural byproduct of the testing procedure. Simulation studies and two real data analyses demonstrate the gains of the new testing procedure over those based either on categorization or on regression models.
Authors:
L Peng; J P Fine
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2008-02-11
Journal Detail:
Title:  Biometrics     Volume:  64     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2008 Dec 
Date Detail:
Created Date:  2008-11-26     Completed Date:  2008-12-31     Revised Date:  2013-05-20    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1080-9     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, Emory University, Atlanta, Georgia 30322, USA. lpeng@sph.emory.edu
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MeSH Terms
Descriptor/Qualifier:
Biometry / methods*
Computer Simulation
Humans
Regression Analysis
Statistics, Nonparametric*
Survival Analysis*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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