The concept of mediation has broad applications in medical health studies. Although the statistical assessment of a mediational effect under the normal assumption has been well established in linear structural equation models (SEM), it has not been extended to the general case where normality is not a usual assumption. In this paper, we propose to extend the definition of mediational effects through causal inference. The new definition is consistent with that in linear SEM and does not rely on the assumption of normality. Here, we focus our attention on the logistic mediation model, where all variables involved are binary. Three approaches to the estimation of mediational effects-Delta method, bootstrap, and Bayesian modelling via Monte Carlo simulation are investigated. Simulation studies are used to examine the behaviour of the three approaches. Measured by 95 per cent confidence interval (CI) coverage rate and root mean square error (RMSE) criteria, it was found that the Bayesian method using a non-informative prior outperformed both bootstrap and the Delta methods, particularly for small sample sizes. Case studies are presented to demonstrate the application of the proposed method to public health research using a nationally representative database. Extending the proposed method to other types of mediational model and to multiple mediators are also discussed.
Copyright 2004 John Wiley & Sons, Ltd.