More than 1100 articles now appear annually investigating “independent risk factors” or “independent predictors” for various clinical outcomes. In medical research, independence is generally defined in a statistical sense: a variable is called an independent risk factor if it has a significant contribution to an outcome in a statistical model that includes established risk factors. As such, independence is based on a specific statistical model and depends on the set of established risk factors included in that model. Even when strong statistical evidence indicates that a variable is an independent risk factor for an outcome, this does not necessarily indicate that the risk factor causally contributes to the outcome. The opposite is also true: risk factors that have causal relationships with the outcome will not necessarily prove to be independent risk factors. These are basic statistical principles that are too often given short shrift in medical research. Herein, we discuss the clinical implications conferred by the above definition of independence, primarily using examples from recent cardiovascular literature. A glossary and schema are provided to help clinicians and researchers understand and discuss these matters effectively.
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Arrows denote causation in this idealized example to illustrate the distinctions between 3 types of risk factors. Bidirectional arrows, which may exist in real life, are not included, and this example does not include all possible relationships. In medical research, it may be impossible to discern whether a particular risk factor is a direct causal risk factor, a noncausal risk factor, or an indirect causal risk factor or if it falls into more than 1 category.
Hypothetical model with 2 uncorrelated direct causal factors, 3 noncausal risk factors, and an outcome. All variables are binary. Odds ratios (ORs) define relationships between the variables. Percentages define prevalence rates for the risk factors and the incidence rate for the outcome. For simplicity, bidirectional arrows and interaction terms are not included in this idealized example. Table 3 lists power probabilities for various statistical models applied to this scenario.
Hypothetical model in which a therapy affects a noncausal risk factor and a causal factor. When multiple clinical variables are favorably affected by an efficacious therapy, it may be impossible to discern which of these variables are causal for the outcome.
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