01 A Likelihood Approach to Meta-analysis Accounting for Different Diagnostic Thresholds

Oregon Convention Center 

In many clinical contexts, there are several competing definitions for a single clinical diagnosis. This creates difficulties for synthesizing published research results. When using existing methods for meta-analysis, researchers must either ignore the different definitions or split the analyses based on definition. We propose a model that not only enables meta-analysis across different diagnostic thresholds, but leverages overlapping regions to indirectly estimate parameters.
We propose a likelihood approach for meta-analysis of aggregate results that parameterizes the observed data as mixtures from regions that partition a latent diagnostic variable. We consider difficulties with parameter identifiability and propose a solution using augmentary data. We illustrate our approach with a worked example that estimates the risk of cardiovascular disease among individuals with prediabetes, under two commonly-used definitions. We present extensive simulation models that consider the bias and coverage of our approach in relatively small samples. This approach is a first step toward a more general model for meta-analysis in the face of varying clinical definitions.

mixture model

meta-analysis

prediabetes

cardiovascular disease 

Biometrics Section