Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership

• Published in: Statistica Sinica Vol. 27; no. 4; p. 1857
• Main Authors: Wang, Qianqian, Ma, Yanyuan, Wang, Yuanjia
• Format: Journal Article
• Language: English
• Published: China (Republic : 1949- ) 01.10.2017
• Subjects: Censored data; mixed populations; uncertain population identifier; semiparametric models; Laplace transformation; EM algorithm; transformation models
• DOI: 10.5705/ss.202016.0199
• ISSN: 1017-0405

Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to identify the covariates that have different effects or common effects in distinct populations, which enables parsimonious modeling and better understanding of the difference across populations. The methods are illustrated through extensive simulation studies and a real data example.