Setting the Stage

This chapter presents a detailed rationale for why this volume is needed. The discussion is divided into five sections: (1) a critical overview of the religion-and-health literature is provided; (2) some preliminary observations are made on the state of current theoretical frameworks and conceptual models in the field; (3) a new conceptual model is introduced—this model is based on the premise that religion is, in essence, a social phenomenon that serves as a key conduit for the transmission of core religious virtues; (4) a new model-building strategy is illustrated by showing how submodels (i.e., brief supplementary models) can be used to expand the conceptual scope of the core model; and (5) an overview is provided of the chapters that follow.

Auxiliary Modeling Procedures

Chapter 5 describes three sets of auxiliary methods that have emerged as add-on supplements to the traditional ARIMA model-building strategy. First, Bayesian information criteria (BIC) can be used to inform incremental modeling decisions. BICs are also the basis for the Bayesian hypothesis tests introduced in Chapter 6. Second, unit root tests can be used to inform differencing decisions. Used appropriately, unit root tests guard against over-differencing. Finally, co-integration and error correction models have become a popular way of representing the behavior of two time series that follow a shared path. We use the principle of co-integration to define the ideal control time series. Put simply, a time series and its ideal counterfactual control time series are co-integrated up the time of the intervention. At that point, if the two time series diverge, the magnitude of their divergence is taken as the causal effect of the intervention.

Bayesian binary regression model: an application to in-hospital death after AMI prediction

A Bayesian binary regression model is developed to predict death of patients after acute myocardial infarction (AMI). Markov Chain Monte Carlo (MCMC) methods are used to make inference and to evaluate Bayesian binary regression models. A model building strategy based on Bayes factor is proposed and aspects of model validation are extensively discussed in the paper, including the posterior distribution for the c-index and the analysis of residuals. Risk assessment, based on variables easily available within minutes of the patients' arrival at the hospital, is very important to decide the course of the treatment. The identified model reveals itself strongly reliable and accurate, with a rate of correct classification of 88% and a concordance index of 83%.