The Future of Observational Epidemiology: Improving Data and Design to Align With Population Health.

Abstract

Improvements in data resources and computational power provide important opportunities to ensure the continued relevance and growth of observational epidemiology. To achieve that promise, rigorous statistical analyses are important but not sufficient. We must prioritize articulating relevant research questions and developing strong study designs. Relevance depends on designing observational research so it delivers actionable clinical or population health evidence. Expanding data sources, including administrative records and data from emerging technologies such as sensors, can potentially be leveraged to improve study design, statistical power, measurement, and availability of evidence on diverse populations. With these advantages, particularly evidence on the heterogeneity of treatment effects, observational research can better guide design of randomized trials. Evidence on the heterogeneity of treatment effects is also essential to extend the evidence from randomized trials beyond the narrow range of settings and populations for which trials have been conducted. Machine learning tools will likely grow in importance in observational epidemiology in coming years, although we need careful attention to the appropriate uses of prediction models. Despite the potential of these innovations, they will only be useful if embedded in theoretical frameworks motivated by applied clinical and population health questions.