A methodology for measuring the relative effectiveness of health services

In this paper we propose a methodology for measuring the 'relative effectiveness' of healthcare services (i.e. the effect of hospital care on patients) under general conditions, in which: ®) a healthcare outcome underlies qualitative and quantitative observable indicators; b) we are interested in studying the simultaneous dependency of covariates from multiple outcomes, which can also be correlated to each other; c) the relative effectiveness is adjusted for hospital-specific covariates; d) we hypothesize a general distribution for random disturbances and the random parameters of relative effectiveness. For this topic, a generalization of the SURE (Seemingly Unrelated Regression Equations) Multilevel Model is proposed. The solutions are obtained by means of Bayesian inference methods. Since there is currently no software available to estimate this model, an SAS procedure based on MCMC (Markov Chain Monte Carlo) methods has been developed by the authors, in line with Goldstein and Spiegelhalter (1996) and Spiegelhalter et al. (1996). In addition, a new theoretical result regarding the joint posterior distribution of the parameters is provided. The model proposed has been implemented for an effectiveness study of a selection of Lombard hospitals