Abstract Number:
3435
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Deanna Marriott (1), John Jardine (2), Ivy Isaman (2)
Institutions:
(1) N/A, N/A, (2) University of Michigan, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
mixture model|meta-analysis|prediabetes|cardiovascular disease| |
Sponsors:
Biometrics Section
Tracks:
Risk Prediction
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