Advancing Principled Pharmacoepidemiologic Research to Support Regulatory and Healthcare Decision Making: The Era of Real-World Evidence.

A compilation of factors over the past decade-including the availability of increasingly large and rich healthcare datasets, advanced technologies to extract unstructured information from health records and digital sources, advancement of principled study design and analytic methods to emulate clinical trials, and frameworks to support transparent study conduct-has ushered in a new era of real-world evidence (RWE). This review article describes the evolution of the RWE era, including pharmacoepidemiologic methods designed to support causal inferences regarding treatment effects, the role of regulators and other health authorities in establishing distributed real-world data networks enabling analytics at scale, and the many global guidance documents on principled methods of producing RWE. This article also highlights the growing opportunity for RWE to support decision making by regulators, health technology assessment groups, clinicians, patients, and other stakeholders and provides examples of influential RWE studies.

Real Effect or Bias? Good Practices for Evaluating the Robustness of Evidence From Comparative Observational Studies Through Quantitative Sensitivity Analysis for Unmeasured Confounding.

The assumption of "no unmeasured confounders" is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains under-utilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements for application of each method. With the advent of methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder-along with publicly available code for implementation-roadblocks toward broader use of sensitivity analyses are decreasing.

Novel molecular resources for single-specimen barcoding of enigmatic crustacean y-larvae.

Despite discovery more than 100years ago and documented global occurrence from shallow waters to the deep sea, the life cycle of the enigmatic crustacean y-larvae isincompletely understood and adult forms remain unknown. To date, only 2 of the 17 formally described species, all based on larval stages, have been investigated using an integrative taxonomic approach. This approach provided descriptions of the morphology of the naupliar and cyprid stages, and made use of exuvial voucher material and DNA barcodes.

Power to the people: why person-generated health data are important for pharmacoepidemiology.

Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc).