Could simulation methods solve the curse of sparse data within clinical studies of antibiotic resistance?

How to apply structural equation modelling to infectious diseases concepts

BACKGROUND

Structural equation modelling (SEM) can address causation questions of great interest to infectious disease physicians and infection control practitioners that would elude techniques based on tests of association. Questions such as the size of intervention effects mediated on entities that cannot be easily measured, questions that cannot be studied in randomized controlled trials and question arising from 'big data'.

OBJECTIVES

To outline the computational and, moreover conceptual, differences between SEM methods versus the traditional tests of association.

SOURCES

Google scholar search for "structural equation modelling" and " Infection" CONTENT: Several examples of SEM applications to infectious diseases topics are used to illustrate. The SEM technique enables postulated causation models to be confronted with data. With this, the candidate models emerge as either 'importantly wrong', or potentially useful for enabling empiric predictions from the one identified as optimal.

IMPLICATIONS

Applications of SEM techniques and related modelling techniques to infectious diseases research will likely continue to emerge, especially so with the availability of 'big data'. ''Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.'' [George Box; 1].