PROJECT SUMMARY/ABSTRACT The primary objective of the current proposal is to foster efforts towards transparent and reproducible knowledge repositories for evidence-based medicine. The wealth of healthcare data already available in electronic health records could be better utilized to help guide treatment choices and compliment findings from randomized controlled trials. This proposal addresses two major obstacles. The first is the challenge of deriving high-quality evidence from observational data in the presence of biases and confounders, particularly with temporal data. The second is that patient privacy and other concerns prevent disclosure of source data, which hinders reproducible research -- currently there is a vast body of medical literature whose findings guide clinical practice, yet cannot be independently scrutinized. We will address these challenges through an innovative methodology, local control, which both corrects biases and enables disclosure of question-specific microaggregated data to reproduce research findings without disclosure of individual information. The key idea behind local control is to form many homogeneous patient clusters within which one can compare alternate treatments, statistically correcting for measured biases and confounders, analogous to a randomized block design. Our methodology provides a unified framework for enabling open, high quality, comparative effectiveness research by combining novel feature selection approaches, based on fractional factorial experimental design, with advances in survival analysis, including competing risks. We will create a public R package containing a family of methods for nonparametric bias correction and statistical disclosure control in cross-sectional, case-control, and survival analysis settings. Success of this research will also enable a novel model, we term “parcelled data sharing” to facilitate open selective release of proprietary data sources for specific questions -- simultaneously protecting patient privacy, proprietary interests, and the public good. Our research will contribute to the goal of evidence-based medicine being supported by national and global knowledge bases on thousands of comparative effectiveness questions from 100’s of millions of patients’ health records. This application supports the NLM mission by assisting in the advancement of medical and related sciences through the dissemination and exchange of important information to the progress of medicine and health. The specific aims are to (1) Develop and evaluate a survival-based local control methodology for bias-corrected treatment comparisons in time-to-event observational data; and (2) Develop and evaluate local control- based microaggregation for reproducible research.

PROJECT NARRATIVE Electronic health records (EHRs) are available for millions of patients and offer a wealth of information that could be used to guide treatment decisions and choices, particularly in groups that are often not included in clinical trials. However, EHRs are observational data, and as such, can contain biases that may make drawing definitive conclusions challenging. To address these issues, we are developing methods that can analyze existing health records to aid in predicting the effects of different treatments. We are also developing methods that can allow health information to be released for reproducing research findings, while protecting patient privacy.