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.

翻译：回归方法在生物统计学分析实践中占据主导地位，但生物统计学培训往往过于强调回归模型与方法的细节，而忽视了此类建模可能适用的实际目的。更广泛而言，统计学被普遍理解为提供了一套用于“数据建模”的技术体系，其背后隐含着我们称之为“真实模型迷思”的理念：即统计学家/数据分析师的任务是构建一个能紧密逼近真实数据生成过程的模型。通过我们自身的历史案例以及对主流临床研究期刊的简要回顾，我们阐述了这一视角如何导致回归方法应用中的一系列问题，包括对协变量的错误“调整”、回归系数的误读，以及广泛存在的缺乏明确目的而拟合回归模型的现象。随后，我们提出了一种生物统计学方法教学与应用的新路径，将其置于一个框架之中，该框架首先要求根据以下三个类别之一明确定义实质性的研究问题：描述性、预测性或因果性。在此框架下，简单的单变量回归模型可作为描述性工具引入，而多变量回归模型及其他高级生物统计学方法的开发与应用，则应根据问题类型采取不同的处理方式。回归方法无疑仍将是统计实践的核心，因为它们为表征响应或结局变量随“输入”变量变化的规律提供了有力工具，但其概念化与具体使用方式应始终服务于实际的研究目的。