Titlebook: Real-World Evidence in Medical Product Development; Weili He,Yixin Fang,Hongwei Wang Book 2023 The Editor(s) (if applicable) and The Autho

FADE

沙发

Privacy-Preserving Record Linkage for Real-World Dataic health records (EHRs), claims activities, and patient registries. It is of great interest to aggregate and link data of the same patients from several data sources to provide a more comprehensive longitudinal evaluation of treatments from different aspects. Privacy-Preserving Record Linkage (PPRL

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小说

Estimand in Real-World Evidence Study: From Frameworks to Applicationon five attributes—target population, treatment, endpoint, intercurrent events, and population-level summary. Although the addendum states that the principles are also applicable to single-arm trials and observational studies, constructing estimands for real-world evidence (RWE) studies might requir

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GEON

Innovative Hybrid Designs and Analytical Approaches Leveraging Real-World Data and Clinical Trial Dareatment effectiveness and safety. RWD can serve to augment the trial data in a variety of ways including the analysis population, the treatments considered, the variables on each subject, or the follow-up time. These approaches include using RWD as external controls to complement single arms trials

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Statistical Challenges for Causal Inference Using Time-to-Event Real-World Datal trials, or to augment a small internal control arm in a phase II trial. Using RWD for causal inference is a difficult task due to the lack of treatment randomization. RWD with time-to-event (henceforth referred to as TTE RWD) outcomes present extra challenges since causal effect estimands for TTE

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Sensitivity Analyses for Unmeasured Confounding: This Is the Waye assumption of ‘no unmeasured confounders’. While researchers have long been aware of the potential bias from unmeasured confounders, recently many new approaches have been proposed to quantitatively assess the robustness of research to the potential for unmeasured confounding. These include a grow

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Sensitivity Analysis in the Analysis of Real-World Datan estimator to deviations from its underlying modeling assumptions and limitations in the data.” We start with an anatomy of potential assumptions behind an answerable research question. Then, we discuss how to conduct sensitivity analysis to explore the robustness of inferences to deviations from t