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We study the influence of explanatory variables in prediction by looking at the distribution of the log-odds ratio. We also consider the predictive influence of a subset of unobserved future variables on the distribution of log-odds ratio as well as in a logistic model, via the Bayesian predictive density of a future observation. This problem is considered for dichotomous, as well as continuous explanatory variables. AMS Subject Classification: Primary 62J12, Secondary 62B10, 62F15Keywords: Predictive density/probability; Log-odds ratio; Logistic model; Predictive influence; Missing/unobserved variable; Kullback-Leibler divergence in dealing with 2×2 tables in biomedical studies and clinical trials. The distribution of the log of sample OR is often approximated by a normal distribution with true log OR as the mean and with variance estimated by the sum of the reciprocal of the four cell frequencies in the 2×2 [5] have considered the problem of the influence of observations for logistic regression models. Several measures have been suggested to identify observations in the data set which are influential relative to the estimation of the vector of regression coefficients, the deviance, the determination of predictive probabilities and the classification of future observations.Bhattacharjee & Dunsmore [6] considered the effect on the predictive probability of a future observation of the omission of subsets of the explanatory variables. Mercier et al. [7] used logistic regression to determine whether age and/or gender were a factor influencing severity of injuries suffered in head-on automobile collisions on rural highways. Zellner et al. [8] considered the problem of variable selection in logistic regression to compare the performance of stepwise selection procedures with a bagging method.In the present paper, our aim is to measure the predictive influence of a subset of explanatory variables in log-odds ratio of a logistic model using a Bayesian approach. We are also interested in studying the effect of missing future explanatory variables on Bayes prediction, on a logistic model as well as on the log-odds ratio.In Section 2, we derive the predictive densities of a future logodds ratio for both the full model and a subset deleted model. We derive the predictive density of log-odds ratio in Section 3, when a subset of future explanatory variables is missing. To derive the predictive densities we assume that the future explanatory variables f x are distributed as multivariate normal, both when these x f 's are independent or dependent. In Section 4, we discuss the influence of future missing explanatory variables by considering the predictive probability of a future response in a logistic model. This is done by assuming that the future explanatory variables f x are multivariate normal for the continuous case. Also considered is the dichotomous case. Since the predictive probabilities are not mathematically tractable for the logistic model, we use several approximations.In Section 2 and 3 we employ Kullback...