# Sieve based full likelihood approach for the Cox proportional hazards model with applications to immunotherapies trials

> **NIH NIH R21** · DUKE UNIVERSITY · 2024 · $178,760

## Abstract

In clinical studies, the time-to-event outcomes are widely used to evaluate a new treatment
efficacy. When complete randomization is not possible or successful the Cox proportional
hazards model can be used to adjust for the confounding effects and to increase statistical
power. Phase II studies usually have relatively small number, so the survival analysis results are
not very reliable or do not have enough power to detect a positive result of the experimental
treatment. This project aims to provide better survival analysis results compared to existing
methods for small sample studies. Sieve method has becoming a popular approach in
survival analysis, we propose to use the sieve maximum likelihood estimations to improve
survival analysis results for small studies and provide corresponding computing tools for
public use.

## Key facts

- **NIH application ID:** 10793642
- **Project number:** 5R21CA263950-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** SUSAN HALABI
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $178,760
- **Award type:** 5
- **Project period:** 2023-02-22 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10793642

## Citation

> US National Institutes of Health, RePORTER application 10793642, Sieve based full likelihood approach for the Cox proportional hazards model with applications to immunotherapies trials (5R21CA263950-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10793642. Licensed CC0.

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