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Instrumental Variable Estimation of Complier Causal Treatment Effect with Interval-Censored Data

Principal Investigator
Name
Jianguo Sun
Degrees
Ph.D
Institution
University of Missouri-Columbia
Position Title
Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-871
Initial CDAS Request Approval
Nov 30, 2021
Title
Instrumental Variable Estimation of Complier Causal Treatment Effect with Interval-Censored Data
Summary
Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous treatment selection to attain unbiased estimation of causal treatment effect. Existing development of IV methodology, however, hasn't attended to outcomes subject to interval censoring, which are ubiquitously present in studies with intermittent follow-up but are challenging to handle in terms of both theory and computation. In this work, we fill in this important gap by studying a general class of causal semiparametric transformation models
with interval-censored data. We propose a nonparametric maximum likelihood estimator of the complier causal treatment effect. Moreover, we design a reliable and computationally stable EM algorithm which has a tractable objective function in the maximization step via the use of Poisson latent variables. The asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, are established with empirical process techniques. We conduct extensive simulation studies and an application to a colorectal cancer screening dataset, showing satisfactory finite-sample performance of the proposed method as well as its prominent advantages over naive methods.
Aims

1. filling the research gap of studying a general class of causal semiparametric transformation models with interval-censored data.
2. We will propose a nonparametric maximum likelihood estimator of the complier causal treatment effect.
3. we will design a reliable and computationally stable EM algorithm which has a tractable objective function in the maximization step via the use of Poisson latent variables.
4. developing the asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, will be established with empirical process techniques.

Collaborators

Shuwei Li, School of Economics and Statistics, Guangzhou University, China.
Limin Peng, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, U.S.A email: lpeng@emory.edu