  We introduce a new data fusion method that utilizes multiple data sources to
estimate a smooth, finite-dimensional parameter. Most existing methods only
make use of fully aligned data sources that share common conditional
distributions of one or more variables of interest. However, in many settings,
the scarcity of fully aligned sources can make existing methods require unduly
large sample sizes to be useful. Our approach enables the incorporation of
weakly aligned data sources that are not perfectly aligned, provided their
degree of misalignment can be characterized by a prespecified density ratio
model. We describe gains in efficiency and provide a general means to construct
estimators achieving these gains. We illustrate our results by fusing data from
two harmonized HIV monoclonal antibody prevention efficacy trials to study how
a neutralizing antibody biomarker associates with HIV genotype.

Methodology