Addressing data privacy in matched studies via virtual pooling

• Published in: BMC medical research methodology Vol. 17; no. 1; pp. 136 - 10
• Main Authors: Saha-Chaudhuri, P., Weinberg, C.R.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.09.2017; BioMed Central; BMC
• Subjects: Adult; Analysis; Bias; Biometrics; Blood Pressure; Case-Control Studies; Computer Simulation; Conditional logistic regression; Confidentiality; Data analysis; Data Anonymization; Data integrity; Data Interpretation, Statistical; Data privacy; Disease; Distributed data network; Efficiency; Epidemiologic Methods; Epidemiology; Female; Health care; Humans; Information sharing; Logistic regression; Male; Matched case-control design; Meta-analysis; Middle Aged; Models, Statistical; Multicenter Studies as Topic; Obesity - physiopathology; Privacy; Regression Analysis; Specimen Handling; Specimen pooling; Statistical analysis; Virtual Reality; Conditional logistic regression; Data privacy; Specimen pooling; Matched case-control design; Distributed data network
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-017-0419-0

Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distributed regression, meta-analysis and score-based methods can have important limitations. We propose a novel application of an existing epidemiologic tool, specimen pooling, to enable confidentiality-preserving analysis of data arising from a matched case-control, multi-center design. Instead of pooling specimens prior to assay, we apply the methodology to virtually pool (aggregate) covariates within nodes. Such virtual pooling retains most of the information used in an analysis with individual data and since individual participant data is not shared externally, within-node virtual pooling preserves data confidentiality. We show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratios of interest. The parameter estimates from the standard conditional logistic regression are compared to the estimates based on a conditional logistic regression model with aggregated data. The parameter estimates are shown to be similar to those without pooling and to have comparable standard errors and confidence interval coverage. Virtual data pooling can be used to maintain confidentiality of data from multi-center study and can be particularly useful in research with large-scale distributed data.