Choosing Exploratory, Predictive, or Causal Multivariable Models in Biomedical Research: A Practical Methodological Guide - Vera-Ponce, Victor Juan; Ballena-Caicedo, Jhosmer (International Journal of Statistics in Medical Research, 2026)
Background: Multivariable regression is widely used in biomedical research, but models built for different purposes are often treated as if they were interchangeable. This confuses variable handling, covariate adjustment, model evaluation, and interpretation. Objective: To provide a practical guide for clinicians, biomedical researchers, and collaborating statisticians on how to choose and report multivariable models according to whether the aim is exploratory, predictive, or causal. Methods: We prepared a narrative methodological tutorial using a targeted search of PubMed, Scopus, and Google Scholar, together with key textbooks and reporting guidance (STROBE, TRIPOD+AI, and PROBAST+AI). We prioritized seminal papers and recent methodological references (2021-2025) on variable prespecification, continuous predictors, validation, calibration, and causal diagrams. Illustrative examples are simulated and are used only for didactic purposes. Results: The first step is to state the analytic objective explicitly. Exploratory models are used to describe adjusted associations and generate hypotheses; predictive models aim to estimate individual risk and therefore require attention to discrimination, calibration, and internal/external validation; causal models aim to estimate an effect and should rely on temporality, substantive knowledge, and directed acyclic graphs (DAGs) to define adjustment sets. Across objectives, arbitrary dichotomization and univariable screening are discouraged. Continuous predictors should usually be kept on their original scale, with flexible functions such as restricted cubic splines when nonlinearity is plausible. Penalization is generally preferable to stepwise procedures when overfitting is a concern in prediction, whereas full theory-based models are often preferable in causal analyses. Conclusions: The research question should determine the model, not the reverse. A practical workflow is to define the objective first, prespecify candidate variables, choose a functional form that preserves information, and evaluate the model with objective-specific criteria. Clear separation of exploratory, predictive, and causal aims improves transparency, interpretability, and clinical usefulness.