# Modeling Multi-Source Data in Hodgkin Lymphoma

> **NIH NIH R01** · TUFTS MEDICAL CENTER · 2022 · $827,059

## Abstract

ABSTRACT
 Hodgkin lymphoma (HL) is associated with excellent cure rates. Despite early disease control, this success
comes at considerable cost, with treatment-induced morbidity (“late effects” [LE]) that manifests as early as a
year after therapy (e.g., 2nd cancers, cardiovascular [CVD] disease), compromised health-related quality of life
(HRQL), and early mortality. Over the past 20 years, while important HL clinical trials have been conducted,
there has been a lack of consensus about the preferred approach to treatment, considering both short- and
longer term outcomes. While clinical trials provide rich information about short term disease outcomes, they do
not follow efficacy beyond 5 years and rarely track post-therapy morbidity. In contrast, HL registries capture
longitudinal outcomes and can be linked to other sources including healthcare utilization via administrative
claims and cancer registries to characterize LE and survival. This proposal represents an unprecedented
opportunity to draw on the collective expertise of International clinical HL oncology experts, epidemiologists,
imaging experts, and methods & modeling experts with access to individual patient data from modern HL clinical
trials and real-world registries from across the world. In preparation for the project, we harmonized individual
patient data (IPD) for >12,000 HL patients from 12 clinical trials and 4 large HL registries. Building on data
science principles, we established a common data model & unifying data dictionary, which allows us to expand
the current database over time, incorporating future data as they become available. We will harness these multi-
source data to first, create a modern prediction model via predictive modeling of pre-treatment clinical factors;
second, we will estimate and validate disease progression and early emergent late effects, enhanced by the
addition of interim PET imaging data and alternative treatment options via multi-state Cox proportional hazards
models to estimate transition probabilities; and third, we will generate a dynamic decision model to project
precalculated outcomes of interest, such as short term and longer term outcomes, including HRQL, The
aggregated, “standardized” precalculated visualizations/projections (life expectance and quality-adjusted life
years) will provide a basis for comparing alternative treatments for a broad range of disease subgroups and
patient characteristics “on the fly.” Furthermore, both the results of the statistical models & parameter estimates
from the simulation model will be validated and re-calibrated as needed in large external cohorts (i.e., German
Hodgkin Study Group (GHSG) clinical trials data and Dutch HL registry). The simulation model will then be
converted to open source, which will allow local analyses that go beyond our precalculated standard scenarios.
Our three-step approach (predictive modeling to multi-state modeling to simulation/decision modeling) will be
designed to incorpora...

## Key facts

- **NIH application ID:** 10441776
- **Project number:** 1R01CA262265-01A1
- **Recipient organization:** TUFTS MEDICAL CENTER
- **Principal Investigator:** Andrew M Evens
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $827,059
- **Award type:** 1
- **Project period:** 2022-03-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441776

## Citation

> US National Institutes of Health, RePORTER application 10441776, Modeling Multi-Source Data in Hodgkin Lymphoma (1R01CA262265-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10441776. Licensed CC0.

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