Causal modeling and alternative medicine

Abstract

Many problems of health promotion or prevention call for an understanding of relations among variables embedded in complex causal webs that may include psychosocial, cultural, or environmental factors as well as biological dysfunction. Experimental investigation of these kinds of research problems is frequently impossible or not feasible. Causal modeling, particularly latent variable structural modeling, can provide a useful alternative to manipulative experimentation when one is trying to build and test explanatory models in a rigorous and systematic fashion. A hypothetical model of fibromyalgia is presented to illustrate how latent variable models can prove useful when the malady under investigation is of relatively complex multifactorial origin. Topics discussed include the fundamental notion underlying causal models, how such models solve problems due to measurement error, and why both cross-sectional and longitudinal models deserve consideration. A number of applications in medical research are described.