Building reliable evidence from realworld data: Methods, cautiousness and recommendations

Routinely stored information on healthcare utilisation in everyday clinical practice has proliferated over the past several decades. There is, however, some reluctance on the part of many health professionals to use observational data to support healthcare decisions, especially when data are derived from large databases. Challenges in conducting observational studies based on electronic databases include concern about the adequacy of study design and methods to minimise the effect of both misclassifications (in the absence of direct assessments of exposure and outcome validity) and confounding (in the absence of randomisation). This paper points out issues that may compromise the validity of such studies, and approaches to managing analytic challenges. First, strategies of sampling within a large cohort, as an alternative to analysing the full cohort, will be presented. Second, methods for controlling outcome and exposure misclassifications will be described. Third, several techniques that take into account both measured and unmeasured confounders will also be presented. Fourth, some considerations regarding random uncertainty in the framework of observational studies using healthcare utilisation data will be discussed. Finally, some recommendations for good research practice are listed in this paper. The aim is to provide researchers with a methodological framework, while commenting on the value of new techniques for more advanced users.