# Bayesian Methods for Complex Precision Biotherapy Trials in Oncology

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $363,164

## Abstract

Project Summary/Abstract
Most clinical trial designs use \one-size- ts-all" rules for treatment assignment and evaluation based on models that
ignore patient heterogeneity. This is disconnected from medical practice, where physicians use each patient's diagnosis
and prognostic variables to make personalized, precision medicine treatment decisions. Modern precision medicine
exploits biotechnologies such as proteomics, genomics, gene sequencing, mass spectrometry, or cytometry methods
that evaluate multiple cell surface markers. These generate vectors of biomarkers that may be used to re ne existing
disease subgroup de nitions, construct new disease classi cations, and formulate clinical trial designs and statistical
rules for personalized/precision treatment assignment. In oncology and other disease areas, there is rapidly increasing
development of new biotherapies, including cell therapies, immunotherapies, and targeted molecular agents. A biotherapy
may be administered once or in multiple cycles; used in combination with conventional treatments such as cytotoxic
chemotherapy, radiation, or surgery; and often generates complex outcomes, such as repeatedly evaluated tumor status,
multiple biological variables, and occurrence times of both early and late onset toxicities. This complicates the de nitions
of \response" and \toxicity," and produces multidimensional treatment e ects that may di er between subgroups. An
example is a phase I-II trial to optimize subgroup-speci c doses of donor derived natural killer (NK) cells for treating
B-cell hematologic malignancies, where donated NK cells are engineered using chimeric antigen receptors to enhance
their cancer killing e ects, then expanded using growth factors to obtain cell doses large enough for therapeutic use.
Subgroups may be de ned using disease subtypes and prognostic variables. Co-primary outcomes may include ordinal
disease status, including complete or partial remission, stable disease, or disease progression, evaluated either once or at
monthly intervals; time to severe NK cell-related toxicity, such as cytokine release syndrome; and a binary indicator of
100-day survival. Considering (biotherapy, dose, administration schedule) a treatment regime, a clinical trial of one or
more new biotherapies may include a subgroup-speci c risk-bene t tradeo based dose or schedule optimization for each
biotherapy, randomization among regimes restricted to achieve balance within subgroups, and subgroup-speci c group
sequential rules to select superior regimes or drop unsafe or ine ective regimes. The proposed research will construct
robust Bayesian regression models for regime-outcome e ects that account for patient heterogeneity, including possible
regime-subgroup interactions. These will be the basis for sequential decision making and regime assignment, and
they may include latent variables to adaptively combine subgroups with similar regime-outcome e ects. Each clinical
trial design will be tailored ...

## Key facts

- **NIH application ID:** 10489366
- **Project number:** 5R01CA261978-02
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Ruitao Lin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $363,164
- **Award type:** 5
- **Project period:** 2021-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10489366

## Citation

> US National Institutes of Health, RePORTER application 10489366, Bayesian Methods for Complex Precision Biotherapy Trials in Oncology (5R01CA261978-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10489366. Licensed CC0.

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