|Title||Maximum likelihood estimation for semiparametric transformation models with interval-censored data.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Zeng, Donglin, Lu Mao, and D Y. Lin|
|Date Published||2016 Jun|
Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand.
|Original Publication||Maximum likelihood estimation for semiparametric transformation models with interval-censored data.|
|PubMed Central ID||PMC4890294|
|Grant List||R01 CA082659 / CA / NCI NIH HHS / United States |
R37 AI029168 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI050410 / AI / NIAID NIH HHS / United States
R01 AI029168 / AI / NIAID NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
Maximum likelihood estimation for semiparametric transformation models with interval-censored data.