# ODAC: learning from local to global - an efficient algorithm for integrating time to event healthcare system data

## Rui PhD1, PhD2, PhD1 Rui PhD1, PhD2, PhD1 Duan, Chongliang Luo, Martijn J. Schuemie, Yong Chen, 1University 2Janssen of Pennsylvania, Philadelphia, PA; Research and Development, Raritan, NJ Is this the first time you have submitted your work to be displayed at any OHDSI Symposium? Yes \_\_x\_\_\_ No\_\_\_\_\_\_ Abstract

### Institutional data integration becomes a trend in EHR-based studies, as it improves the confidence of study results by increasing the sample size and also reduce potential biases that come from disparities in patient population or clinical practice. To overcome the barrier of patient-level data sharing, we propose a privacy-preserving and communication-efficient distributed algorithm for Cox proportional hazard model to study the time to event outcome. The performance of the algorithm is investigated through simulation study and application to datasets from the Observational Health Data Sciences and Informatics (OHDSI). The results demonstrate that the proposed algorithm can provide relatively accurate estimation when studying rare events.

## Introduction

Multicenter analysis confers distinct advantages over single-center studies, including the ability to study rare exposures or outcomes which require larger sample sizes and discover more generalizable findings. Direct sharing of patient-level data across institutions is impractical due to considerations on both privacy protection and communication cost. Current distributive networks, such as the Observational Health Data Sciences and Informatics (OHDSI), often rely on sharing summary statistics across sites and synthesizing evidence through meta-analyses. However, in the case where the outcomes or exposures are rare, or some of the sites have limited sample size, accuracy of meta-analysis is not guaranteed. We propose a One-shot Distributed Algorithm for Cox regression (ODAC) by extending the surrogate likelihood function (2018)3, approach proposed by Jordan et al. and the One-shot Distributed Algorithm for Logistic regression4. We investigate the performance of ODAC by comparing it to the commonly used meta- analysis and the gold standard estimator where all data are pooled together.

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$$Assume we only have the patient level data of the “local” site, say site 1. We propose a distributed$$