Invited commentary: simple models for a complicated reality.

Abstract

The renowned statistician George P. Box famously said that all models are wrong, but some are useful. Far from an indictment of statistical models, Box's statement can be taken to mean that even when complex realities are not exactly represented by simple fitted models, much can be learned. The paper by Basso et al. (1) in this issue of the Journal provides an opportunity to consider the costs and benefits that arise from the simplification necessary for generating statistical models of complex biologic processes. Considering the relation among birth weight, mortality, and third factors, Basso et al. postulate that birth weight is not itself on the causal path to mortality; rather, the relation between birth weight and mortality might be explained by a confounding factor. The authors conclude that, to produce the observed inverse J shape of the birth-weight-specific mortality curve, the putative confounding factors (matrix X = (X₁ and X₂)) must be very rare and have very large effects.