  The traditional approach of health risk modelling with multiple data sources
proceeds via regression-based methods assuming a marginal distribution for the
outcome variable. The data is collected for $N$ subjects over a $J$ time-period
or from $J$ data sources. The response obtained from $i^{th}$ subject is
$\vec{Y}_i=({Y}_{i1},\cdots, {Y}_{iJ})$. For $N$ subjects we obtain a $J$
dimensional joint distribution for the subjects. In this work we propose a
novel approach of transforming any $J$ dimensional joint distribution to that
of a $J$ dimensional Gaussian keeping the Shannon entropy constant. This is in
stark contrast to the traditional approaches of assuming a marginal
distribution for each $Y_{ij}$ by treating the $Y_{ij}'$s as independent
observations. The said transformation is implemented in our computer package
called ENTRA.
