R01HL159077

Bayesian Machine Learning for Causal Inference with Incomplete Longitudinal Covariates and Censored Survival Outcomes - Project Summary

Population cohort studies funded by the National Institute of Health, including the Atherosclerosis Risk in Communities (ARIC) study and Multi-Ethnic Study of Atherosclerosis (MESA), are widely used in cardiovascular research and have provided fundamental knowledge for cardiovascular disease (CVD) prevention strategies and public health policies.

Pooling data across multiple cohorts provides a unique opportunity for in-depth investigations of emerging CVD research questions, such as optimal blood pressure threshold values triggering initiation of antihypertensive treatment for young adults, that heretofore would not have been possible. While forming a fertile ground for innovative research, the methodological issues associated with the pooled cohorts data cannot be as effectively addressed by existing statistical methods. There are three main analytic challenges.

First, many discrete or continuous longitudinal variables have missing values with various missing data patterns. Existing methods either are susceptible to misspecification biases or do not provide coherent estimates of imputation uncertainty, and cannot handle missing not at random.

Second, current causal inference methods either require aligned measurement time points or parametric assumptions about forms of causal pathways, neither of which can be satisfied in complex longitudinal health data.

Third, violations of the "sequential ignorability" assumption embedded in causal inference methodology can be a potential source of bias. The sensitivity analysis methods for time-varying confounding with censored survival outcomes are underdeveloped.

To overcome these challenges and improve statistical and CVD research, we propose a suite of generalizable statistical methods utilizing machine learning. We propose to develop a scalable Bayesian nonparametric (BNP) framework to impute continuous or discrete missing at random longitudinal covariates while providing coherent uncertainty intervals, and address the missing not at random mechanism via sensitivity analysis. We will apply the developed method to address missing data issues for several longitudinal CVD risk factors such as blood pressure, cholesterol levels (Specific Aim 1).

To develop a robust and computationally efficient BNP causal inference method (Specific Aim 2) and a new continuous-time marginal structural survival model from a Bayesian perspective (Specific Aim 3) to study and validate the survival effects of time-varying antihypertensive treatments for young adults and the frail elderly.

To develop a flexible and interpretable survival sensitivity analysis method to assess the sensitivity of the causal effect estimates to varying degrees of sequential unmeasured confounding (Specific Aim 4).

And to create usable R software packages for all proposed methods and develop tutorial papers and short courses to bridge theoretical and practical knowledge and promote use of our methods (Specific Aim 5).

Rutgers The State University Of New Jersey

THE NATIONAL HEART, LUNG, AND BLOOD INSTITUTE (NHLBI) PROVIDES GLOBAL LEADERSHIP FOR A RESEARCH, TRAINING, AND EDUCATION PROGRAM TO PROMOTE THE PREVENTION AND TREATMENT OF HEART, LUNG, AND BLOOD DISEASES AND ENHANCE THE HEALTH OF ALL INDIVIDUALS SO THAT THEY CAN LIVE LONGER AND MORE FULFILLING LIVES. TO FOSTER HEART AND VASCULAR RESEARCH IN THE BASIC, TRANSLATIONAL, CLINICAL AND POPULATION SCIENCES, AND TO FOSTER TRAINING TO BUILD TALENTED YOUNG INVESTIGATORS IN THESE AREAS, FUNDED THROUGH COMPETITIVE RESEARCH TRAINING GRANTS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION; USE SMALL BUSINESS TO MEET FEDERAL RESEARCH AND DEVELOPMENT NEEDS; FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY SOCIALLY AND ECONOMICALLY DISADVANTAGED PERSONS; AND INCREASE PRIVATE-SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION; FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE R&D BETWEEN SMALL BUSINESSES AND RESEARCH INSTITUTIONS, AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL R&D.

93.837 - Cardiovascular Diseases Research

National Heart Lung and Blood Institute (NHLBI) [HHS - NIH]

Newark, New Jersey 071073001 United States

Single Zip Code

NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed) (PA-20-185)

Amendment Since initial award the total obligations have increased 354% from $722,305 to $3,275,727.