Confusion in evaluating research quality comes from lack of clarity: The case for explicit research questions.

Abstract

A recent news article suggested observational studies confuse correlation and causation. Such confusion often arises from unclear research aims rather than inherent limitations of study design. We use this recent public critique of epidemiologic research to illustrate how ambiguity in study questions contributes to misinterpretation of findings. We argue that epidemiologic studies generally fall into three distinct categories (descriptive, predictive, and causal) and that each has different goals, assumptions, analytic approaches, and criteria for evaluation. Descriptive studies characterize the distribution of health outcomes, predictive studies aim to identify who will experience those outcomes, and causal studies seek to estimate the effects of interventions or exposures under well-defined counterfactual contrasts. Failure to clearly state which of these aims is being pursued makes it difficult to evaluate methods, assess validity, and interpret results. It also creates opportunities for critiques that may mischaracterize study intent or overstate limitations. We emphasize that observational studies can contribute to causal inference when aligned with explicit causal questions and supported by appropriate assumptions and design choices. We conclude that explicitly stating study aims and estimands would improve scientific communication, facilitate more appropriate critique, and strengthen the contribution of epidemiologic evidence to public health decision-making.