# Cancer Emulation Analysis with Deep Neural Network

> **NIH NIH R03** · YALE UNIVERSITY · 2023 · $167,500

## Abstract

Project Summary
To objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer
prognosis, rigorously designed and executed randomized clinical trials (RCTs) remain the gold standard.
However, as exemplified in this application and many published studies, RCTs are not always feasible.
Fortunately, the fast development of electronic medical records and insurance claims databases has made it
possible to mine a large amount of observational data and efficiently complement RCTs. This strategy has been
enthusiastically endorsed by multiple national organizations. Among the available observational data analysis
techniques that aim to draw RCT-type conclusions, emulation has emerged as especially appealing, with its trial-
like architecture, interpretability, and scalability. It has been applied to multiple cancers and other complex
diseases and led to clinically significant findings.
 This study has two equally important aims. The first aim is to develop deep neural network (DNN)-based
emulation analysis methods and software. Most of the existing emulation analyses are based on classic
regression techniques. Compared to regression, DNN excels with superior model fitting and higher flexibility.
Recently, our group was the first to develop a DNN-based emulation analysis approach and applied it to
cardiovascular diseases. Advancing from this recent success, we will develop more interpretable and more
stable DNNs tailored to RCT analysis. We will then further expand the analysis scope and conduct DNN-based
analysis of a sequence of emulated trials. For both a single emulated trial and a sequence of trials, we will
develop valid inference, which is essential for RCT analysis but has been neglected in most DNN studies. User-
friendly software will be developed. This methodological development will substantially expand the scope of
emulation analysis, deep learning, causal inference, observation data analysis, and medical record/insurance
claims data analysis. The second aim is to develop and analyze two emulated trials. We will address the
comparative effectiveness of (a) lobectomy and limited resection on lung cancer survival for the SEER-Medicare
elderly population, and (b) radical prostatectomy and observation on localized prostate cancer survival for the
VA population. The findings will be comprehensively and rigorously evaluated. To provide a more comprehensive
picture, we will also analyze using multiple alternative methods and compare against existing RCTs and
observational studies. With the significant methodological advancements and powerful data, our analysis will
lead to more definitive findings, directly inform clinical practice, and serve as the prototype for future applications.

## Key facts

- **NIH application ID:** 10725293
- **Project number:** 1R03CA276790-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Shuangge Ma
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $167,500
- **Award type:** 1
- **Project period:** 2023-09-19 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10725293

## Citation

> US National Institutes of Health, RePORTER application 10725293, Cancer Emulation Analysis with Deep Neural Network (1R03CA276790-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10725293. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*