Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting.

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.

The Structured Process to Identify Fit-For-Purpose Data: A Data Feasibility Assessment Framework.

To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions.

Real-world reproducibility study characterizing patients newly diagnosed with multiple myeloma using Clinical Practice Research Datalink, a UK-based electronic health records database.

Purpose: We evaluated the reproducibility of a study characterizing newly-diagnosed multiple myeloma (MM) patients within an electronic health records (EHR) database using different analytic tools.

Methods: We reproduced the findings of a descriptive cohort study using an iterative two-phase approach. In Phase I, a common protocol and statistical analysis plan (SAP) were implemented by independent investigators using the Aetion Evidence Platform® (AEP), a rapid-cycle analytics tool, and SAS statistical software as a gold standard for statistical analyses.

Characterization of Missing Data in Clinical Registry Studies.

Patterns of missing data are seldom well-characterized in observational research. This study examined the magnitude of, and factors associated with, missing data across multiple observational studies. Missingness was evaluated for demographic, clinical, and patient-reported outcome (PRO) data from a procedure registry (TOPS), a rare disease (cystic fibrosis) registry (Port-CF), and a comparative effectiveness registry (glaucoma, RiGOR).

Why observational studies should be among the tools used in comparative effectiveness research.

Doctors, patients, and other decision makers need access to the best available clinical evidence, which can come from systematic reviews, experimental trials, and observational research. Despite methodological challenges, high-quality observational studies have an important role in comparative effectiveness research because they can address issues that are otherwise difficult or impossible to study. In addition, many clinical and policy decisions do not require the very high levels of certainty provided by large, rigorous randomized trials.