  Regression methods dominate the practice of biostatistical analysis, but
biostatistical training emphasises the details of regression models and methods
ahead of the purposes for which such modelling might be useful. More broadly,
statistics is widely understood to provide a body of techniques for "modelling
data", underpinned by what we describe as the "true model myth": that the task
of the statistician/data analyst is to build a model that closely approximates
the true data generating process. By way of our own historical examples and a
brief review of mainstream clinical research journals, we describe how this
perspective has led to a range of problems in the application of regression
methods, including misguided "adjustment" for covariates, misinterpretation of
regression coefficients and the widespread fitting of regression models without
a clear purpose. We then outline a new approach to the teaching and application
of biostatistical methods, which situates them within a framework that first
requires clear definition of the substantive research question at hand within
one of three categories: descriptive, predictive, or causal. Within this
approach, the simple univariable regression model may be introduced as a tool
for description, while the development and application of multivariable
regression models as well as other advanced biostatistical methods should
proceed differently according to the type of question. Regression methods will
no doubt remain central to statistical practice as they provide a powerful tool
for representing variation in a response or outcome variable as a function of
"input" variables, but their conceptualisation and usage should follow from the
purpose at hand.
