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Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies

Award Number: R01HL166324
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/01/2023
PERIOD OF PERFORMANCE END DATE: 02/28/2027

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Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies - Project Summary This proposal will develop Bayesian machine learning approaches via Bayesian nonparametrics (BNP) to handle nonignorable missingness (in outcomes and covariates) and conduct causal inference for electronic health records (EHRs), to address missingness in multivariate longitudinal data, and for causal mediation problems. Missing data remains a problem in clinical studies and in particular, for studies using EHRs. In clinical studies, more effort is spent to try to minimize the amount of missingness, but it still remains a problem and missingness is a constant issue (and less controllable) in studies based on EHRs. In addition, there has been limited work on the use of auxiliary information in EHRs that can enhance the ability to deal with missing data. Approaches for missingness in multivariate longitudinal data is underdeveloped and relevant across many clinical trials settings from cost effectiveness analysis to incomplete time-varying auxiliary covariates (or confounders) to causal mediation to multiple outcomes of interest. The mechanisms of treatment effectiveness are of particular interest in behavioral trials. Specifically, how do different processes mediate the effect of an intervention? This can facilitate constructing future interventions. However, determining the causal effect of such 'mediators' on outcomes is difficult. We will develop new approaches to identify these effects in the complex setting of cluster randomized trials for which little work has been done. For all these settings, a Bayesian approach is ideal as it allows one to appropriately characterize uncertainty about unverifiable assumptions (which are present in all these problems) and allows the flexibility of Bayesian nonparametric models. MCMC algorithms for BNP can sometimes converge slowly and can be untenable for large n. We will extend existing approaches to address both these complications which will be important for all the applications considered and in general, given the increasing size and complexity of data. The methods are motivated by several NHLBI funded studies, whose PJ's are co-investigators on this proposal, and will be developed to help answer numerous important clinical questions including the mechanisms of behavior change in weight management and the impact of linkage ( and engagement) to care on treatment effectiveness for blood pressure outcomes. The methods will also help us evaluate potentially synergistic effects when drugs with potential diabetogenic effects are used concomitantly and whether the impact on cancer outcomes varies by different bariatric surgeries. The history of the the collaborations among the entire study team will help produce the best science and facilitate dissemination of our methodological and clinical findings. We will disseminate code for these methods (via the PJ's github page and software papers) to ensure the methods will be readily usable by investigators involved in cardiovascular, obesity, diabetes, and cancer studies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $486,028 )
2025	2025	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cardiovascular Diseases Research	000	3	3/11/2025	NON-COMPETING CONTINUATION	$486,028
														Subtotal = $486,028

Issue Date FY: 2024 ( Subtotal = $465,349 )
2024	2024	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cardiovascular Diseases Research	001	2	6/21/2024	NON-COMPETING CONTINUATION	$37,988
2024	2024	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cardiovascular Diseases Research	000	2	2/29/2024	NON-COMPETING CONTINUATION	$427,361
														Subtotal = $465,349

Issue Date FY: 2023 ( Subtotal = $548,304 )
2023	2023	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cardiovascular Diseases Research	000	1	2/23/2023	NEW	$548,304
														Subtotal = $548,304

Grand Total All Awards = $1,499,681

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Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies

Award Number: R01HL166324

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 03/01/2023

PERIOD OF PERFORMANCE END DATE: 02/28/2027

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