  Background: In the context of ongoing debate over data confidentiality versus
shared use of research data, as raised following the new EU General Data
Protection Regulation, we seek to find alternate techniques that can balance
these two issues. In particular, we demonstrate that an existing epidemiologic
tool, specimen pooling, can be adapted as a privacy-preserving method to enable
data analysis while maintaining data confidentiality. Specimen pooling is a
cost-effective tool in studying the effect of an expensive-to-measure exposure
on a disease outcome, for both unmatched and matched case-control designs. We
propose the technique in a new context to analyze confidential data and
demonstrate that it can be successfully used to estimate OR of covariates based
on aggregate data when individual patient data cannot be shared.
  Methods: We demonstrate the application of specimen pooling based on
aggregate covariate level and show that aggregated covariate levels can be used
in a conditional logistic regression model to estimate individual-level odds
ratio parameters of interest. We then show how to adapt the technique as a
privacy-preserving method to analyze data from a matched case-control design. A
similar approach can be applied for an unmatched design and unconditional
logistic regression.
  Results: The parameter estimates from the standard conditional logistic
regression are compared to those based on aggregated data. The parameter
estimates are similar and have similar standard errors and confidence interval
coverage.
  Conclusions: Pooling can be used effectively to analyze confidential data
arising from distributed data networks and will be extremely useful in
pharmacoepidemiology.
