# Risk prediction of progression, recurrence, and death after acute ischemic stroke

> **NIH NIH R03** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $155,334

## Abstract

Project Summary/Abstract
Motivated by the repeated and mixed types of post stroke readmission events, this proposal
addresses the development and application of novel statistical approaches for the analysis of
multivariate recurrent event data. It also develops a predictive model which allows the
incorporation of multiple short-term events data for the prediction of survival outcomes.
Existing approaches in stroke application usually (1) failed to consider the recurrent nature of
the readmission data, or (2) considered only type of event or a composite endpoint by
combining preventable and unpreventable events for analysis. These simplified approaches
result in biased assessment of true disease burden. Therefore, to provide accurate
understanding, it is important to study different causes of readmission events simultaneously.
The commonly used models are shared random effect models with an assumption of constant
dependence between different types of recurrent events over time. However, this assumption is
not satisfied and subsequently the existing models are not adequate to model the data with
time-varying dependence. To address this challenge, in Aim 1, we plan to develop an innovative
joint modeling approach for multivariate recurrent event data allowing for time-varying
dependence over time.
To improve the prediction accuracy of long-term survival outcomes, it would be desirable to
incorporate short-term event outcomes along with biological markers for risk prediction. Existing
methods can only handle a single short-term event, which may have poor prediction
performance when multiple short-term events are available and associated with survival
outcomes. In Aim 2, we plan to develop a novel predictive tool which quantifies the risk of long-
term outcomes by incorporating multiple short-term outcomes into prediction framework.
These proposed approaches will address a gap in statistical analysis in the context of
multivariate recurrent event data and provide a better predictive tool for survival outcomes.

## Key facts

- **NIH application ID:** 9895523
- **Project number:** 1R03NS111178-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** MOHAMMAD HOSSEIN RAHBAR
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $155,334
- **Award type:** 1
- **Project period:** 2020-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895523

## Citation

> US National Institutes of Health, RePORTER application 9895523, Risk prediction of progression, recurrence, and death after acute ischemic stroke (1R03NS111178-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9895523. Licensed CC0.

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