A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks

Clin Pharma and Therapeutics

2020

Self Cite

A wide range of analytic and data sharing options are available in nonexperimental multidatabase studies designed to assess the real‐world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options—study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discusses key scientific and practical considerations when choosing among these options in multidatabase studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data‐contributing sites are able or willing to share. While pooling of person‐level data sets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary‐level information may be equally valid and preferred in some multidatabase studies, especially when sharing of person‐level data is challenging or infeasible.

Section: Other Considerations Ease Of Implementationmentioning

confidence: 99%

Analytic and Data Sharing Options in Real‐World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

Clin Pharma and Therapeutics

2020

Self Cite

“…Compared to the pooled individual-level analysis, however, our method requires multiple file transfers between the data partners and the analysis center. Although this need for information exchange at each iteration means that our proposed method is more labor-intensive to implement in practice, recent advancements in bioinformatics now allow semi-automated or fully-automated file transfers between data partners and the analysis center [17, 21–27]. For general users who may not have access to such technical infrastructure, we have developed R code that allows manual implementation of our proposed method.…”

Section: Discussionmentioning

confidence: 99%

Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies

Shu

Young

2019

BMC Med Res Methodol

Self Cite

BackgroundMulti-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies.MethodsBy leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis.ResultsOur proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites.ConclusionsWe developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

“…Manual exchanges of this summary-level information can be too tedious and labor-intensive to be practical in actual multicenter studies. However, there are a number of statistical packages and standalone software that enable researchers to perform distributed regression and partially or fully automate the file transfer process 19,20,31–33. We chose not to assess the operational performance (eg, runtime) of our distributed regression analysis because it is highly network-dependent.…”

Section: Discussionmentioning

confidence: 99%

Combining distributed regression and propensity scores: a doubly privacy-protecting analytic method for multicenter research

Wellman

Coley

et al. 2018

CLEP

Self Cite

PurposeSharing of detailed individual-level data continues to pose challenges in multi-center studies. This issue can be addressed in part by using analytic methods that require only summary-level information to perform the desired multivariable-adjusted analysis. We examined the feasibility and empirical validity of 1) conducting multivariable-adjusted distributed linear regression and 2) combining distributed linear regression with propensity scores, in a large distributed data network.Patients and methodsWe compared percent total weight loss 1-year postsurgery between Roux-en-Y gastric bypass and sleeve gastrectomy procedure among 43,110 patients from 36 health systems in the National Patient-Centered Clinical Research Network. We adjusted for baseline demographic and clinical variables as individual covariates, deciles of propensity scores, or both, in three separate outcome regression models. We used distributed linear regression, a method that requires only summary-level information (specifically, sums of squares and cross products matrix) from sites, to fit the three ordinary least squares linear regression models. A comparison set of analyses that used pooled deidentified individual-level data from sites served as the reference.ResultsDistributed linear regression produced results identical to those from the corresponding pooled individual-level data analysis for all variables in all three models. The maximum numerical difference in the parameter estimate or standard error for all the variables was 3×10−11 across three models.ConclusionDistributed linear regression analysis is a feasible and valid analytic method in multicenter studies for one-time continuous outcomes. Combining distributed regression with propensity scores via modeling offers more privacy protection and analytic flexibility.