Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB,ApoA1 and the ratio were determined using a Bayesian approach |
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Authors: | S. Islam S. Anand M. McQueen J. Hamid L. Thabane S. Yusuf |
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Affiliation: | 1. Population Health Research Institute, McMaster University and Hamilton Health Sciences, Ontario, Canada;2. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada;3. Department of Medicine, McMaster University, Hamilton, Ontario, Canada;4. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada;5. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada |
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Abstract: | We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification. |
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Keywords: | Bayesian approach conditional logistic regression ApoB ApoA1 MI AUC |
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