Subgroup analysis with semiparametric models toward precision medicine.
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in medicine"
DOI: 10.1002/sim.7638
Abstract: In analyzing clinical trials, one important objective is to classify the patients into treatment-favorable and nonfavorable subgroups. Existing parametric methods are not robust, and the commonly used classification rules ignore the fact that the implications… read more here.
Keywords: medicine; medicine subgroup; subgroup analysis; semiparametric models ... See more keywords
A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data
Sign Up to like & getrecommendations! Published in 2017 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2017.1356316
Abstract: ABSTRACT A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all… read more here.
Keywords: arbitrarily censored; censored survival; survival data; semiparametric models ... See more keywords
Copula-based semiparametric models for spatiotemporal data.
Sign Up to like & getrecommendations! Published in 2019 at "Biometrics"
DOI: 10.1111/biom.13066
Abstract: The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatiotemporal model for analyzing spatiotemporal… read more here.
Keywords: copula based; method; spatiotemporal data; semiparametric models ... See more keywords
Non-iterative adjustment to regression estimators with population-based auxiliary information for semiparametric models.
Sign Up to like & getrecommendations! Published in 2021 at "Biometrics"
DOI: 10.1111/biom.13585
Abstract: Disease registries, surveillance data, and other data sets with extremely large sample sizes become increasingly available in providing population-based information on disease incidence, survival probability or other important public health characteristics. Such information can be… read more here.
Keywords: regression estimators; information; population based; semiparametric models ... See more keywords