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Generating real-world evidence in Alzheimer's disease: Considerations for establishing a core dataset. | LitMetric

AI Article Synopsis

  • - Ongoing assessment in postapproval studies for Alzheimer's disease aims to track disease progression and evaluate the effectiveness and safety of treatments in real-world scenarios.
  • - The study faces challenges due to differences in data collection methods across various centers and the diversity of patients compared to those in clinical trials.
  • - Key design elements for these studies include specifying aims and objectives, identifying study populations, and establishing consistent methods for measuring cognition, function, safety, and quality of life.

Article Abstract

Ongoing assessment of patients with Alzheimer's disease (AD) in postapproval studies is important for mapping disease progression and evaluating real-world treatment effectiveness and safety. However, interpreting outcomes in the real world is challenging owing to variation in data collected across centers and specialties and greater heterogeneity of patients compared with trial participants. Here, we share considerations for observational postapproval studies designed to collect harmonized longitudinal data from individuals with mild cognitive impairment or mild dementia stage of disease who receive therapies targeting the underlying pathological processes of AD in routine practice. This paper considers key study design parameters, including proposed aims and objectives, study populations, approaches to data collection, and measures of cognition, functional abilities, neuropsychiatric status, quality of life, health economics, safety, and drug utilization. Postapproval studies that capture these considerations will be important to provide standardized data on AD treatment effectiveness and safety in real-world settings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11180865PMC
http://dx.doi.org/10.1002/alz.13785DOI Listing

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