# Robust Causal Comparisons of Nonrandomized Oncology Studies

> **NIH NIH R21** · GEORGETOWN UNIVERSITY · 2022 · $215,910

## Abstract

Robust Causal Comparison of Nonrandomized Groups in Oncology Studies
The goal of our research is to develop robust statistical models for causal comparison of nonrandomized groups. With the
increasing availability of real world data (RWD), the trial design to compare a new therapy versus an external control
obtained via RWD (e.g., EHR) has received renewed attention recently. This design has been utilized in therapeutic
development especially in rare diseases when RCT is not feasible, e.g., comparing 3-year relapse-free survival (RFS)
between locally treated high-risk ocular melanoma patients on adjuvant combination immunotherapy versus a matched
contemporaneous control obtained outside of the trial. Then the challenge is at making causal inference on whether the
treatment is efficacious in prolonging (e.g.) patient survival. But which method? Causal inference is known to depend on
various assumptions. Despite advances in making various causal inference methods robust, e.g., most notably the doubly
robust estimate (DRE) existing DREs continue to suffer several major drawbacks, e.g., being too sensitive to mild model
misspecifications. Our preliminary studies on enhanced DREs that shows the needed robustness in making causal
inference realized through semi-parametric models for trials limited with continuous primary endpoint. Built upon this
development, the goal of this application is to develop the novel DRE approach for analyzing nonrandomized clinical trials
with binary (such as response in oncology, incidence in epidemiology) and time to event (such as survival and progression
free survival in oncology) outcomes and assess their statistical properties, which as is well known can be quite different from
the proof of principle case with continuous outcome (Aims 1-2). The methods will then be applied to an NCI sponsored trial,
and a population science study and one ongoing immunotherapy trial.

## Key facts

- **NIH application ID:** 10434299
- **Project number:** 1R21CA270585-01
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** MING Tony TAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $215,910
- **Award type:** 1
- **Project period:** 2022-04-27 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10434299

## Citation

> US National Institutes of Health, RePORTER application 10434299, Robust Causal Comparisons of Nonrandomized Oncology Studies (1R21CA270585-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10434299. Licensed CC0.

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