Associated-Factor Studies in Biomedical Research: Foundations, Limitations, and Criteria for Appropriate Use

Associated-factor studies constitute a substantial portion of the observational epidemiology literature. However, their proliferation has raised important methodological concerns. Many are published without a clear research question, repeat well-established associations, or use causal language to interpret coefficients from exploratory models. This narrative methodological review and critical reflection provides guidance for determining when associated-factor studies add genuine scientific value, how to plan sample size using events per variable, and how to interpret and report results appropriately. We review the nature of exploratory models and how they differ from predictive and causal models. We examine the Table 2 fallacy and its implications for interpreting adjusted coefficients. We discuss the events per variable criterion as a framework for sample-size planning in multivariable studies, present illustrative examples of both appropriate and inappropriate uses of these analyses, and provide an operational decision and minimum-reporting checklist for authors, reviewers, and editors. Associated-factor studies have a legitimate role when the phenomenon is poorly understood, when there is a genuine evidence gap in a truly different population, or when the explicit aim is hypothesis generation rather than causal inference. Their scientific contribution is limited when they merely confirm well-established associations, use causal language without a causal design, or are conducted simply because data are available.