# Matched Design with Sensitivity Analysis for Observational Survival Data in Cardiovascular Patient Management using EMR Data

> **NIH NIH R21** · OHIO STATE UNIVERSITY · 2024 · $113,035

## Abstract

Time-to-event is a ubiquitous outcome measure in clinical diagnosis and assessment of therapeutic effects in
many disease areas including stroke (time-to-stroke), respiratory (time-to-first medication for worsening asthma)
and sleep diseases (time-to-insomnia-related mortality). The hazard rate is commonly seen in survival analysis
as it has the convenient interpretation of instantaneous risk. The hazard ratio (HR) is routinely used as an effect
measure when comparing between two treatment groups, largely due to the popular Cox proportional hazards
(PH) model. However, the HR is vulnerable to selection bias and not collapsible, which make it a questionable
marginal causal effect measure. Restricted mean survival time (RMST) is an alternative measure, defined as the
area under the survival curve up to a fixed time point. RMST difference is a more adequate causal effect measure
than the HR because (i) it is a collapsible measure, thus avoids discrepancy between marginal and conditional
effects; (ii) it does not depend on the PH assumption; (iii) it is essentially a mean difference with simpler
interpretation. RMST has become a popular metric of treatment effects in randomized trials recently. However,
the development of RMST methodology for observational survival data is lacking. The goal of this proposal is
to develop a comprehensive matching-based RMST difference estimation strategy to infer causal effects
in observational survival data, and apply such tools to evaluate causal effects of direct oral
anticoagulants (DOAC) vs. warfarin on the risk of cardiovascular events in a secondary data analysis.
We plan to develop propensity score matching-based RMST estimation methodology and corresponding
sensitivity analysis, which do not rely on strong outcome modeling assumptions. The matching method will use
an optimal algorithm to create matched sets to mimic a block randomized design and an asymptotically valid
post-matching inferential procedure will be developed by accounting for the correlation introduced in matching.
Built upon the matched data, the sensitivity analysis will address how much association an unmeasured
confounder would need to have with both the exposure and the outcome, to explain away the observed effect.
In the secondary data analysis, we will apply our methods to examine the hypothesis that using DOAC has lower
risk of composite cardiovascular events including stroke, venous thromboembolism, myocardial infarction, and
death, using electronic medical record (EMR) data. We will also explore subgroup causal effects related to
gender and race to examine potential health disparity issues. Our proposed work will not only result in novel and
valid research methodology for estimating causal effects in observational survival data, but also advance the
understanding of how different anticoagulant drugs would impact patient outcomes using a large secondary
database. Our general-purpose methodology will be widely applicable to study surviva...

## Key facts

- **NIH application ID:** 10894124
- **Project number:** 5R21HL170212-02
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Bo Lu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $113,035
- **Award type:** 5
- **Project period:** 2023-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10894124

## Citation

> US National Institutes of Health, RePORTER application 10894124, Matched Design with Sensitivity Analysis for Observational Survival Data in Cardiovascular Patient Management using EMR Data (5R21HL170212-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10894124. Licensed CC0.

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