# Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $336,555

## Abstract

Project Summary:
Modern cardiovascular (CV) trials often collect data on a wide array of fatal and nonfatal events (e.g., heart
failure, heart attack, stroke, chest pain, and etc.) with different implications for patient health. In recent years,
new methods have started to emerge which seek to capture more events than the traditional endpoint of each
patient’s first event. However, to account for the totality of a composite endpoint while differentiating the
importance of its components (e.g., death vs CV hospitalization) is not easy. As it stands, investigators still lack
adequate tools to measure treatment effects, design future trials, assess risk factors, and build prediction models.
In this project, we address these gaps via four specific aims. In Aim 1, we consider a general class of
nonparametric effect-size estimands defined though pairwise comparison (both overall and subgroup-wise), in
which one component can be readily prioritized over another using a hierarchical rule of comparison. The inverse
probability censoring weighting (IPCW) and augmented inverse probability weighting (AIPW) techniques are
adapted to U-statistic estimators to correct for censoring bias and to improve efficiency (and thus reduce trial
cost) using patient data both pre- and post-randomization. In Aim 2, we develop routines to calculate power and
sample size for newly proposed methods for composite endpoints, such as the restricted mean time in favor of
treatment and while-alive loss rate, under both fixed and group sequential designs. In Aim 3, we propose novel
semiparametric regression models for composite endpoints following earlier work on the proportional win-
fractions (PW) model. In particular, the generalized semiparametric proportional odds (GSPO) model
accommodates nonproportional win fractions by extending traditional PO models to multiple events with ordered
severities. In Aim 4, we extend survival trees as a predictive tool from univariate to composite endpoints. Drawing
on classification trees for ordinal response, we develop time-integrated versions of the weighted Gini index and
twoing approach for node-splitting, and of a generalized concordance index for cross-validative pruning, thereby
accounting for both the timing and severity of the outcome events. The methods developed will be used for
secondary analyses of the recently concluded INfluenza Vaccine to Effectively Stop cardio-Thoracic Events and
Decompensated heart failure (INVESTED) trial (ClinicalTrials.gov: NCT02787044). Meanwhile, they will be
incorporated into new and existing R-packages on the Comprehensive R Archive Network (CRAN.R-project.org)
for public use by practitioners.

## Key facts

- **NIH application ID:** 10912794
- **Project number:** 5R01HL149875-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Lu Mao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $336,555
- **Award type:** 5
- **Project period:** 2019-12-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912794

## Citation

> US National Institutes of Health, RePORTER application 10912794, Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials (5R01HL149875-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10912794. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*