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

Bayesian Machine Learning for Causal Inference with Incomplete Longitudinal Covariates and Censored Survival Outcomes is sponsored by National Heart, Lung, and Blood Institute (NHLBI). Develops Bayesian machine learning methods for causal inference in cardiovascular research with incomplete longitudinal data and censored survival outcomes.

##### Population cohort studies funded by the National Institute of Health, including the Atherosclerosis Risk in Com- munities (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 investiga- tions 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 misspeci? cation biases or do not provide coherent estimates of imputation un- certainty, 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 satis?

ed 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 chal- lenges 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 con- tinuous 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 (Speci? c Aim 1); to develop a robust and computationally ef? cient BNP causal inference method (Speci?

c Aim 2) and a new continuous-time marginal structural survival model from a Bayesian perspective (Speci? c Aim 3) to study and validate the survival effects of time-varying antihypertensive treatments for young adults and the frail elderly; to develop a ? exible and interpretable survival sensitivity analysis method to assess the sensitivity of the causal effect estimates to varying degrees of sequential unmeasured confounding (Speci?

c 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 (Speci? c Aim 5).

###### **Questionfy****Wikify****Summarize** **administering_ic**NHLBI **application_id**10620291 **award_notice_date**2023-05-01 **cat_norm**MEDICAL SCIENCE|HEART, LUNG & BLOOD **direct_cost_amt**531158 **funding_mechanism**Non-SBIR/STTR **ic_name**NATIONAL HEART, LUNG AND BLOOD INSTITUTE **indirect_cost_amt**135638 **notice_date**2023-05-01 **org_country**UNITED STATES **org_dept**BIOSTATISTICS & OTHER MATH SCI **org_norm**RUTGERS BIOMEDICAL AND HEALTH SCIENCES **phr**"Project Narrative The integration of longitudinal cohort studies such as those funded by the National Heart, Lung, and Blood Insti- tute (NHLBI) has provided cardiovascular researchers an important opportunity to address emerging questions that would not have been answered by a single cohort, but at the same time substantially increases the complex- ity of data structures and demands novel analysis approaches.

We develop a suite of novel Bayesian machine learning methods to address challenges posed by complex longitudinal data with censored survival outcomes, including missing exposure data, causal inference with time-varying confounding and sequential unmeasured confounding.

Our work is uniquely relevant to the mission of NHLBI and is aligned within the institute's strate- gic vision, and will provide a key apparatus for advancing the understanding of emerging cardiovascular health questions via enhanced use of integrated data."

**program_officer_name**"WOLZ, MICHAEL" **project_start**2022-5-15 **project_terms**"Address;Adult;Age;Antihypertensive Agents;Assessment tool;Atherosclerosis Risk in Communities;Bayesian Analysis;Bayesian Method;Bayesian learning;Blood Pressure;Cardiovascular Diseases;Cardiovascular system;Cholesterol;Clinical;Cohort Studies;Complex;Computer software;Data;Data Pooling;Elderly;Estimation Techniques;Frail Elderly;Funding;Goals;Health Policy;Intervention;Investigation;Knowledge;Life Cycle Stages;Longitudinal cohort study;Machine Learning;Methodology;Methods;Mission;Modeling;Multi-Ethnic Study of Atherosclerosis;National Heart, Lung, and Blood Institute;Outcome;Paper;Pathway interactions;Pattern;Population;Predisposition;Prevention strategy;Property;Public Health;Race;Research;Research Personnel;Risk Estimate;Risk Factors;Sample Size;Selection Bias;Software Tools;Source;Specific qualifier value;Standardization;Statistical Methods;Strategic vision;Structure;Time;Time Study;Trees;Uncertainty;United States National Institutes of Health;Weight;Work;cardiovascular disorder prevention;cardiovascular disorder risk;cardiovascular health;cohort;data complexity;data integration;ethnic diversity;flexibility;follow-up;health data;improved;innovation;machine learning framework;machine learning method;novel;population based;software development;survival outcome;temporal measurement;treatment effect;usability;young adult" **study_section**Biostatistical Methods and Research Design Study Section[BMRD] get answers and insights from .

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