DOI: 10.3886/icpsr39563.v1
title: Privacy-Preserving Analytic and Data-Sharing Methods for Clinical and Patient-Powered Data Networks [Methods Study], California, Colorado, and Washington, 2014-2018
title: Version 1

description: Sometimes a study can get better results using data from different sites. In these cases, researchers may want to share patient data, including personal and private information such as dates of birth and addresses. However, researchers may not want to share data across sites because of worries about patient privacy. Some statistical methods can change patients' sensitive individual data into summary data that hides individuals' personal information. These privacy-protecting methods, or PPMs, make it safe to share data across sites. But researchers don't know if PPMs produce accurate results.
In this study, the research team compared combinations of PPMs with methods that use patients' individual data.
 To access the methods, software, and R package, please visit the distributed GitHub.
description: This project aimed to (1) assess stakeholders' understanding of and preference for
privacy-preserving analytic and data-sharing methods, and assess the benefits and limitations
of implementing them in multisite PCOR studies; (2) develop or enhance a suite of privacy-preserving methods to perform rigorous analysis without sharing individual-level data; and (3) create freely available dissemination tools, including analytic code, educational materials, technical documentation, and user guides for these methods.
description: In this study, the research team developed a set of privacy-preserving analytic methods made up of data sharing methods, confounding scores, and confounding adjustment approaches. The team compared the performance of privacy-preserving methods that use summary-level data with those using individual-level data.
The research team conducted multiple simulations to generate individual-level data that resembled patient data from an observational study. Next, the team transformed the individual-level simulated data to create three different summary-level data sets representing increasing levels of summarization: risk-set data, summary-table data, and effect-estimate data.
The research team then analyzed each data set using different privacy-preserving methods to estimate treatment effects on health outcomes. To control for confounding in the analyses, the team applied confounder summary scores such as propensity scores (PSs) and disease risk scores (DRSs). The team incorporated these scores into the analyses for confounding adjustment via matching, stratification, or weighting. To evaluate the performance of the various privacy-preserving methods on survival outcomes, the team compared the treatment effect estimates with the true treatment effect used to generate the simulated data.
The research team also tested the performance of the privacy-preserving methods using real data from two comparative effectiveness studies on obesity and rheumatoid arthritis from three sites within a clinical data research network. The team applied the same analytic methods used with the simulated data.
To design and implement this study, the research team worked with patients, health system administrators, other researchers, and experts in governance and regulatory compliance.
description: Datasets: 
DS1: Privacy-Preserving Analytic and Data-Sharing Methods for Clinical and Patient-Powered Data Networks [Methods Study], California, Colorado, and Washington, 2014-2018
description: Patients from integrated delivery systems - Kaiser Permanente Colorado, Kaiser Permanente Northern California, and Kaiser Permanente Washington.