A multiple-imputation method is developed for analysing data from an observational study where some covariate values are not observed. A hybrid approach is presented where the imputations are created under a Bayesian model involving an extended set of variables, although the ultimate analysis may be based on a regression model with a smaller set of variables. The imputations are the random draws from the posterior predictive distribution of the missing values, given the observed values. Gibbs sampling under an extension of the Olkin-Tate general location-scale model is used for the imputation. The method proposed is used to analyse data from a population-based case-control study investigating the association between drug therapy and primary cardiac arrest among pharmacologically treated hypertensives. The sensitivity of the inference to the assumptions about the mechanism for the missing data is explored by creating imputations under several non-ignorable mechanisms for missing data. The sampling properties of the estimates from the hybrid multiple-imputation approach are compared with those based on the complete data and maximum likelihood approaches through simulated data sets. This comparison suggest that much efficiency can be gained through the hybrid approach. Also, the multiple-imputation approach seems to be fairly robust to departures from the assumed normality unless the actual distribution of the continuous covariates is very skew.