# Modeling and Validation for Tackling Risk Prediction with Competing Risks by Integrating Multiple Longitudinal Biomarkers

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $350,427

## Abstract

ABSTRACT/PROJECT SUMMARY
 Surgical treatments such as transplantation often pose considerable analytic challenges to risk prediction
for mortality. For example, disease prognosis and treatment decisions in pediatric acute liver failure (PALF) calls
for a reliable tool to predict mortality risk. However, the development of this prediction tool is hampered by the
high frequency of liver transplantation (LTx), the occurrence of which modifies the disease course of the patient
and dependently censors the death event of interest. Existing competing risks methods are not well suited to
risk prediction for PALF. Recognizing the substantial prognostic value in multiple longitudinal biomarkers as well
as baseline covariates, we aim to tackle risk prediction in the presence of treatment-induced competing risks by
developing, implementing and applying sensible and computationally feasible modeling, validation and inference
procedures. In this project, (Aim 1) the team proposes a modeling framework that tackles the dependence be-
tween death and LTx through aggregating information from multiple longitudinal and baseline covariates. When
compared to existing methods, the proposed modeling strategy can integrate information from more longitudinal
biomarkers to better capture patients' dynamic disease status. Next, (Aim 2) we propose a comprehensive set
of validation procedures to evaluate prediction performance in the presence of competing risks. The methods
assess prediction performances in both cumulative incidence prediction and marginal probability prediction to
ascertain and enhance prediction performance from all angles. We also develop formal testing procedures
to detect potential predictive heterogeneity among different subtypes of patients. Moreover, we propose (Aim
3) statistical procedures to examine LTx-benefit under a causal inference framework, accommodating subject-
specific benefit to inform personalized LTx decisions. All statistical methods will be rigorously justified through
extensive simulation studies, sensitivity analysis and theoretical derivations, to ensure their theoretical rigor and
practical usefulness. The methods will be systematically applied to a recent PALF registry database. The final
prediction tool will be disseminated to practitioners through a user-friendly web-interface (Aim 4), to facilitate
PALF prediction and dynamic prediction. We anticipate that our methods will be broadly applicable to other
clinical studies and will develop R packages for the broader research community.
1

## Key facts

- **NIH application ID:** 10140329
- **Project number:** 5R01DK117209-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Ruosha Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $350,427
- **Award type:** 5
- **Project period:** 2018-06-19 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10140329

## Citation

> US National Institutes of Health, RePORTER application 10140329, Modeling and Validation for Tackling Risk Prediction with Competing Risks by Integrating Multiple Longitudinal Biomarkers (5R01DK117209-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10140329. Licensed CC0.

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