Evidence triangulation in health research

Abstract

For many important questions about influences on clinical and public health outcomes, no single study can provide a decisive answer. The perfect study—a large, diverse, well-conducted trial randomizing all relevant versions of a treatment and comprehensively tracking all relevant health outcomes—is never feasible. Instead, we must draw conclusions by piecing together evidence from multiple imperfect studies. A systematic framework for combining disparate, complementary sources of evidence is emerging. We introduce this framework, called evidence triangulation; summarize key approaches based on delineating likely biases due to confounding, measurement, and selection; and review some methods for combining evidence. We illustrate the issues using the example of estimating the effects of alcohol use on dementia. The central tenet of evidence triangulation is to identify the most important weaknesses for any given study approach (and for each specific study applying that approach) and, if necessary, to identify which new sources of evidence that do not share these weaknesses are required. Almost certainly, the new studies will have weaknesses, but when results are consistent across studies that rest on different assumptions, and for which biases should be unrelated, the conclusions are on much sturdier ground.