WebDISCO: a web service for distributed cox model learning without patient-level data sharing

Lu, Chia-Lun; Wang, Shuang; Ji, Zhanglong; Wu, Yuan; Xiong, Li; Jiang, Xiaoqian; Ohno-Machado, Lucila

doi:10.1093/jamia/ocv083

Cited by 113 publications

(102 citation statements)

References 22 publications

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“…When done manually, the process can be labor intensive and error prone. To make distributed regression a more practical analytic option in practice, some researchers have developed statistical packages and stand‐alone software to partially or fully automate the file transfer process …”

Section: Other Considerationsmentioning

confidence: 99%

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

Toh

2020

Clin Pharma and Therapeutics

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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.

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Section: Other Considerationsmentioning

confidence: 99%

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

Toh

2020

Clin Pharma and Therapeutics

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“…Each site needs to send the first derivative and Hessian matrices multiple times, which means that a reliable data transmission function is necessary for successfully fitting a grid model. Recently we produced a reliable webservice called WebGLORE for binary logistic grid fitting [20]. In our setting the data transmission was adequate but there may be settings in which this may not be the case.…”

Section: Discussionmentioning

confidence: 99%

Grid multi-category response logistic models

Jiang

Wang

et al. 2015

BMC Med Inform Decis Mak

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BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.MethodsThis paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.ResultsSimulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.ConclusionsThe grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0133-y) contains supplementary material, which is available to authorized users.

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“…This makes DRA a highly desirable analytic method within DDNs. Although researchers have successfully performed DRA in relatively small or simulated multi-database settings [12131415161718192021], routine implementation of the analytic method in practice is challenging. This is because convergence of some regression models common to biomedical research (e.g., logistic and Cox regression) is an iterative process that requires frequent exchanges of intermediate statistics among data partners and an analysis center (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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

Her¹,

Malenfant²,

Malek³

et al. 2018

eGEMs

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Introduction:Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.Objective:We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.Methods:We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.Results:We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.Discussion:The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.Conclusion:DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

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WebDISCO: a web service for distributed cox model learning without patient-level data sharing

Cited by 113 publications

References 22 publications

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

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

Grid multi-category response logistic models

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

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