# Maximum Likelihood Estimation for Semiparametric Transformation Models with Interval-Censored Data

arXiv: https://arxiv.org/abs/1602.02184 · HTML: https://mlsanity.com/paper/1602.02184/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [Deep Partially Linear Transformation Model for Right-Censored Survival Data](https://mlsanity.com/paper/2412.07611/) (2024)
- [Interpretable Deep Regression Models with Interval-Censored Failure Time Data](https://mlsanity.com/paper/2503.19763/) (2025)

## Abstract (arXiv)

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 extensive simulation studies and application to an HIV/AIDS study conducted in Thailand.