Part of the recent literature on the evaluation of surrogate endpoints is started by a definition of validity in terms of both trial-level and individual-level association between a potential surrogate and a true endpoint. In another part, we review the main considerable statistical methods being proposed for the evaluation of a biomarker as surrogate endpoints, which have developed and consider how the validation process might be arranged within the regulatory and practical constraints evaluation. In the present work, we propose a new. Bayesian approach to evaluate individual level surrogacy. Deferent variations to prior distributions were implemented for responses with binomial distribution. Then these methods are compared in a simulation study. Finally, we apply and compare the previous and new methodology using a clinical study.

Sometimes it is impossible to directly measure the effect of intervention (medicine or therapeutic methods) in medical researches. That is because of high costs, long time, the aggressiveness of therapeutic methods, lack of clinical responses, and etc. In such cases, the effect of intervention on surrogate variables is measured. Many statistical studies have been accomplished for measuring the validity of surrogates and introducing a criterion for testing. The first criterion was established based on hypothesis testing. Other criterions were introduced over time. Then by using the classic methods, the Likelihood Ratio Factor was introduced. After that, the Bayesian Likelihood Ratio Factor developed and published. This article aims to introduce the Bayesian Likelihood Ratio Factor based on time dependent data. The illness under study is lung disease in victims of chemical weapons. The surrogate therapy method uses the forced expiratory volume at fist second.

In this paper, the wavelet estimators of the nonparametric regression function based on the various thresholds under the mixture prior distribution and the mean square error loss function in Bosove space are computed. Also, using a simulation study the optimality of different wavelet thresholding estimators such as posterior mean, posterior median, Bayes factor, universal threshold and sure threshold are investigated. The results show that the average mean square error of sure threshold estimator is less than the other obtained estimators.