Jeannie-Marie S Leoutsakos1, Alexandra L Bartlett2, Sarah N Forrester3, Constantine G Lyketsos3. 1. Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: jeannie-marie@jhu.edu. 2. STEM Magnet Program, South River High School, Edgewater, MD, USA. 3. Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Abstract
BACKGROUND: We present a conceptual framework for simulations to determine the utility of biomarker enrichment to increase statistical power to detect a treatment effect in future Alzheimer's disease prevention trials. We include a limited set of simulation results to illustrate aspects of this framework. METHODS: We simulated data based on the Alzheimer's Disease Anti-Inflammatory Prevention Trial, and a range of sample sizes, biomarker positive predictive values, and treatment effects. We also investigated the consequences of assuming homogeneity of parameter estimates as a function of dementia outcome. RESULTS: Use of biomarkers to increase the sample fraction that would develop Alzheimer's disease in the absence of intervention from 0.5 to 0.8 would increase power from 0.35 to 0.69 with n = 200. Ignoring sample heterogeneity resulted in overestimation of power. CONCLUSION: Biomarker enrichment can increase statistical power, but estimates of the expected increase are sensitive to a variety of assumptions outlined in the framework.
RCT Entities:
BACKGROUND: We present a conceptual framework for simulations to determine the utility of biomarker enrichment to increase statistical power to detect a treatment effect in future Alzheimer's disease prevention trials. We include a limited set of simulation results to illustrate aspects of this framework. METHODS: We simulated data based on the Alzheimer's Disease Anti-Inflammatory Prevention Trial, and a range of sample sizes, biomarker positive predictive values, and treatment effects. We also investigated the consequences of assuming homogeneity of parameter estimates as a function of dementia outcome. RESULTS: Use of biomarkers to increase the sample fraction that would develop Alzheimer's disease in the absence of intervention from 0.5 to 0.8 would increase power from 0.35 to 0.69 with n = 200. Ignoring sample heterogeneity resulted in overestimation of power. CONCLUSION: Biomarker enrichment can increase statistical power, but estimates of the expected increase are sensitive to a variety of assumptions outlined in the framework.
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