  In the recent past, electronic health records and distributed data networks
emerged as a viable resource for medical and scientific research. As the use of
confidential patient information from such sources become more common,
maintaining privacy of patients is of utmost importance. For a binary disease
outcome of interest, we show that the techniques of microaggregation
(equivalent to specimen pooling) and \underline{Po}oled \underline{Lo}gistic
\underline{R}egression (PoLoR) could be applied for analysis of large and/or
distributed data while respecting patient privacy. PoLoR is exactly the same as
standard logistic regression, but instead of using individual covariate level,
the analysis uses microaggregated covariate level when microaggregation is
conditional on the outcome status. Aggregate levels of covariates can be passed
from the nodes of the network to the analysis center without revealing
individual-level microdata and can be used very easily with standard softwares
for estimation of disease odds ratio associated with a set of categorical or
continuous covariates. Microaggregation of covariates allows for consistent
estimation of the parameters of logistic regression model that can include
confounders and transformation of exposure. Additionally, since the microdata
can be accessed within nodes, effect modifiers can be accommodated and
consistently estimated. For analysis of confidential health data, covariate
microaggregation for logistic regression will provide a practical and
straightforward alternative to more complicated existing options.
