AbstractGeneration time is a measure of the pace of life and is used to describe processes in population dynamics and evolution. We show that three commonly used mathematical definitions of generation time in age-structured populations can produce different estimates of up to several years for the same set of life history data. We present and prove a mathematical theorem that reveals a general order relation among the definitions. Furthermore, the exact population growth rate at the time of sampling influences estimates of generation time, which calls for attention. For phylogenetic estimates of divergence times between species, included demographic data should be collected when the population growth rate for each species is most common and typical. In conservation biology, demographic data should be collected during phases of population decline in declining species, contrary to common recommendations to use predisturbance data. The results can be used to improve the International Union for Conservation of Nature's recommendation in parameterizing models for evaluating threat categories of threatened species and to avoid underestimating extinction risk.
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Alzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: The ability to monitor cognitive trajectories over the course of trials can provide valuable insights into treatment efficacy. However, existing trial methods are limited in monitoring cognition in real-time and at high frequencies. Gameplay-based assessments hold promise as complementary cognitive tools.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of California, Irvine, Irvine, CA, USA.
Background: Recruitment registries are tools to decrease the time and cost required to identify and enroll eligible participants into clinical research. Despite their potential to increase the efficiency of accrual, few analyses have assessed registry effectiveness. We investigated the outcomes of study referrals from the Consent-to-Contact (C2C) registry, a recruitment registry at the University of California, Irvine.
View Article and Find Full Text PDFBackground: Clinical outcome assessments (COAs) that measure functional capacities are key tools to evaluate efficacy in Alzheimer's disease (AD) clinical trials. The Alzheimer's Disease Cooperative Study Activities of Daily Living (ADCS-ADL) scale is frequently used to assess changes in both basic and instrumental activities of daily living, but there is no clear consensus on what magnitude of change on this scale may be considered clinically meaningful. To address this question, we conducted anchor-based analyses (as recommended by the FDA) to explore meaningful within-patient/participant change thresholds on the ADCS-ADL.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: Clinical trials should strive to yield results that are clinically meaningful rather than solely relying on statistical significance. However, the determination of clinical meaningfulness of dementia clinical trials lacks standardization and varies based on the trial's nature. To tackle this issue, a proposed approach involves assessing the time saved before reaching a specific threshold in cognitive status.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path institute, Tucson, AZ, USA.
Background: To help improve the Alzheimer's disease (AD) therapeutics research and development process, the Critical Path for Alzheimer's Disease (CPAD) Consortium at the Critical Path Institute (C-Path) provides a neutral framework for the drug development industry, regulatory agencies, academia, and patient advocacy organizations to collaborate. CPAD's extensive track record of developing regulatory-grade quantitative drug development tools motivates sponsors to share patient-level data and neuroimages from clinical trials. CPAD leverages these data and uses C-Path's core competencies in data management and standardization, quantitative modeling, and regulatory science to develop tools that help de-risk decision making in AD drug development.
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