Yi-Hsien Lin
Scientific Tracks Abstracts: J Mol Biomark Diagn
I n medical and pharmaceutical research, interest in using biomarkers as surrogate endpoints for target clinical endpoint has stemmed from various reasons. Because of importance of statistical evaluation of surrogate marker, very diff erent methods are suggested. Alonso et al. proposed the ?likelihood reduction factor? (LRF) as a unifi ed approach when neither the biomarker nor the true endpoint is normally distributed. Th is measure of individual- level association can be used under any genera lized linear model for a single trial or meta- analysis. Flowing of these criteria for surrogate evaluation, in this study, we have explored the Bayesian approach to the evaluation of the validity of a surrogate at the individual level, based on the Bayes factor for choosing the best model in the context of generalized linear modeling. It is suggested that the Bayesian LRF denoted by LRFB which benefi ts from the prior knowledge on the situation under study would perform yet better in comparison to other criteria. By a Th eorem we proof, for large sample size, Lim LRF B =LRF. Th e relation between the Bayesian likelihood reduction factor (LRF B ) and its frequentist counterpart (LRF) have been shown by a small scale simulation also. We have simulated diff erent trials with diff erent priors in the logistic regression models by R soft ware. Th e results show that LRF can be viewed as a special case of LRFB relative to a certain prior. Hence, the importance of prior knowledge and Bayesian analysis for surrogate?s validation is shown
Shohreh Jalaie has completed her Ph.D in Biostatistics from Tarbiat Modares University in 2008. She passed a scholarship in Melbourne University. She is faculty member of Tehran University of Medical Science. She has published more than 20 papers in reputed journals.
Molecular Biomarkers & Diagnosis received 2054 citations as per Google Scholar report
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