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Clinical research: benefits of Bhattacharya modeling
Manuel C Solis1, Ton J Cleophas
1Hospital San Carlos, Playa del Carmen, Mexico.
Background:
Bhattacharya modeling is a Gaussian method recommended by the Food and Agricultural Organization of the United Nations Guidelines for analyzing the eco-system. It is rarely used in clinical research.
Objective:
To investigate the performance of Bhattacharya modeling for clinical data analysis.
Methods:
Using as examples simulated vascular lab scores we assessed the performance of the Bhattacharya method. SPSS statistical software is used.
Results:
1. The Bhattacharya method better fitted the data from a single sample than did the usual Gaussian curve derived from the mean and standard deviation with 15 vs. 9 cuts. 2. Bhattacharya models demonstrated a significant difference at p < 0.0001 between the data from two parallel-groups, while the usual t-test and Mann-Whitney test were insignificant at p = 0.051 and 0.085. 3. Bhattacharya modeling of a histogram suggestive of certain subsets identified three Gaussian curves.
Conclusions:
We recommend that Bhattacharya modeling be more often considered in clinical research for the purpose of (1) unmasking normal values of diagnostic tests, (2) improving the p-values of data testing and (3) objectively searching subsets in the data.
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