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

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $368,770

## Abstract

Project Summary:
Many cardiovascular (CV) clinical trials feature complex composite outcomes consisting of multiple types of
(possibly recurrent) events, e.g., heart failure, myocardial infarction, stroke, and death. In addition, due to the
chronic nature of the disease, these long-term trials often suffer from non-randomized cohorts as a result of
informative dropout, a complication that shakes the foundation of randomized controlled trials as the gold
standard for clinical inquiry. Motivated by the INVESTED trial, an ongoing multi-season CV trial comparing two
dosages of influenza vaccine (for which we serve as lead statisticians), this proposal aims to develop novel
statistical methodology that is more robust, more efficient, and better suited for such long-term CV trials. This
goal will be achieved via three specific aims. For specific aim 1, we tackle the problem of non-randomized
cohort adjustment under a comprehensive framework of time-to-event analysis, including the well-known
Kaplan-Meier curve, log-rank test, Cox regression model, and other methods for recurrent events and
competing risks. We will develop a robust inverse probability of treatment weighting (IPTW) approach with non-
/semi-parametrically estimated weights to correct for selection bias in non-randomized cohorts. For specific
aim 2, we generalize the newly developed win-loss approach for composite outcomes from two-sample testing
to the regression setting. The win-loss approach is targeted for composite endpoints consisting of prioritized
components, e.g., death over non-fatal events. The information it extracts from multiple prioritized time-to-
event outcomes is fuller, more interpretable, and clinically more relevant than that contained in time to the first
event, the traditional target of analysis. For specific aim 3, we further generalize the win-loss approach to a
nonparametric framework that allows the win-loss probabilities to depend on the follow-up time. Both
generalizations of the win-loss approach will proceed in an estimand-driven way as recommended by the
recently published ICH-E9(R1) Addendum. Statistical efficiency of the proposed procedures will be studied
thoroughly using modern semiparametric and weak convergence theories. Development of efficient procedures
will help minimize trial costs. User-friendly R packages that implement the algorithms of the proposed methods
will be developed and disseminated through https://cran.r-project.org.

## Key facts

- **NIH application ID:** 10311488
- **Project number:** 5R01HL149875-03
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Lu Mao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $368,770
- **Award type:** 5
- **Project period:** 2019-12-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10311488

## Citation

> US National Institutes of Health, RePORTER application 10311488, Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials (5R01HL149875-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10311488. Licensed CC0.

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