Interpreting multivariable regression coefficients in observational clinical research: The Table 2 fallacy.

Multivariable regression tables are common in observational clinical research, but their coefficients are often over-interpreted. A model built to estimate the effect of 1 exposure may also report coefficients for age, sex, comorbidities, behaviours, and other adjustment variables. These additional rows are frequently read as independent risk factors, even when the analysis was not designed to estimate their effects.

This is the Table 2 fallacy. The problem is not the use of adjustment, but the interpretation of adjustment terms as if each were a separate causal estimate. In this commentary, we use a directed acyclic graph and a single worked example to show why the coefficient for the exposure of interest can answer the intended clinical question, while coefficients for adjustment variables may not represent clinically actionable effects.

We also show how similar errors arise when interaction terms are interpreted as causal. Authors should specify the target estimand (the causal effect the analysis is designed to estimate) and exposure, choose adjustment variables from the assumed causal structure, interpret only the exposure coefficient, and fit a separate model for each further question. We set out red flags, recurring pitfalls, and questions to ask of any risk-factor table.

A regression table is not a menu of modifiable risks. An association is clinically actionable only when the study was designed to support that interpretation.