# Applying statistical learning tools to personalize cardiovascular treatment

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $735,566

## Abstract

Abstract
Cardiovascular disease (CVD) treatment is often guided by risk stratification tools (to decide who to treat), and
randomized controlled trials (to decide which treatments to select). Prior CVD research reveals two major 
obstacles to improving our treatment approach: (i) longitudinal cohort data are unavailable for recalibrating risk
stratification tools for local-area estimation (by zip code), or for people with major CVD-promoting 
comorbidities (e.g., chronic kidney disease); and (ii) the average treatment effect in randomized trials can be
highly erroneous when projected onto individuals that vary from the ‘average’ participant in a trial. CVD risk-
stratification and treatment effect estimation can be improved and personalized if we overcome a critical barrier
to progress: correctly estimating risk and treatment effect from new, large participant data repositories, which
have greater population size and include patients with more co-morbid conditions than common cohort studies,
and which permit personalized risk/benefit prediction tool development from individual-level data. Our prior
studies show that we can critically advance the field by applying novel statistical learning methods to this data,
to address: (i) false-positives from multiple testing; (ii) the reliance on standard regressions that cannot account
for non-linear, complex interactions between factors; and (iii) identifying the optimal approach among many
alternative statistical learning methods. We propose to apply our work in these areas to (Aim 1) Develop CVD
risk stratification tools for patients with inadequate sample sizes in common cohort studies. We will enhance
CVD risk stratification to include local-area adjustment (by zip code) and major co-morbid conditions affecting
CVD risk (e.g., chronic kidney disease). We will additionally (Aim 2) develop personalized treatment effect 
prediction tools to guide decisions for CVD therapies with high potential benefit and risk, for therapies where 
individual participant data from trials are available. We have obtained the individual participant data from the large
randomized trials that reveal wide variations in CVD risk reduction and serious adverse event risk increase
from three drug classes: non-vitamin K antagonist oral anticoagulants, intensive blood pressure treatment, and
sodium-glucose co-transporter 2 inhibitors for diabetes. Our preliminary research shows that traditional 
regression methods cannot distinguish which patients are most likely to benefit or be harmed by such therapies, but
our statistical learning methods can. Finally, we will (Aim 3) develop open-source tools to improve the ability of
researchers to choose an optimal statistical learning approach for their dataset and problem. While numerous
statistical learning methods have been proposed in the literature, a key problem for biomedical scientists 
without access to RCT data is: which method should I use to estimate treatment effects from observa...

## Key facts

- **NIH application ID:** 10356901
- **Project number:** 5R01HL144555-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** NIGAM H SHAH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $735,566
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10356901

## Citation

> US National Institutes of Health, RePORTER application 10356901, Applying statistical learning tools to personalize cardiovascular treatment (5R01HL144555-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10356901. Licensed CC0.

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