|Title||Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Zeng, Donglin, Fei Gao, and D Y. Lin|
|Date Published||2017 Sep|
Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we develop an EM algorithm that converges stably for arbitrary datasets. Finally, we assess the performance of the proposed methods in extensive simulation studies and illustrate their application using data derived from the Atherosclerosis Risk in Communities Study.
|Original Publication||Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data.|
|PubMed Central ID||PMC5787874|
|Grant List||R01 CA082659 / CA / NCI NIH HHS / United States |
R37 AI029168 / AI / NIAID NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
R01 NS073671 / NS / NINDS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data.