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 the Bayesian analysis of contingency tables, analysts commonly use special prior distributions for the parameters of log-linear models or the cell probabilities. But, in practice, sometimes there is some interpretive information which is rather on (generalized) odds ratios. So, it seems one will need a powerful approach so that he can model his prior believe on (generalized) odds ratios. Here, we refer to these priors as structural priors. In this paper we first introduce the general pattern of the structural priors. Then, since these priors have vast application in clinical trials and especially in the analysis of 2 x 2 complete and incomplete contingency tables, we obtain the corresponding structural priors, separately, under three conditions.