On the uses and abuses of regression models: a call for reform of statistical practice and teaching

TL;DR

This paper critiques current regression practices in biostatistics, highlighting issues caused by the 'true model myth' and advocates for a purpose-driven approach categorizing research questions as descriptive, predictive, or causal.

Contribution

It proposes a reform in biostatistical education and practice by aligning regression model use with clearly defined research purposes, improving interpretation and application.

Findings

Current regression practices often mislead due to the 'true model myth'

Misinterpretation of regression coefficients is common in practice

A purpose-driven framework improves regression application

Abstract

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 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.