Cancer Emulation Analysis with Deep Neural Network

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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.