Premium
Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: Conceptual framework and example
Author(s) -
Leoutsakos JeannieMarie S.,
Bartlett Alexandra L.,
Forrester Sarah N.,
Lyketsos Constantine G.
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2013.05.1776
Subject(s) - biomarker , dementia , homogeneity (statistics) , statistical power , disease , predictive power , alzheimer's disease , medicine , statistics , oncology , mathematics , biology , biochemistry , philosophy , epistemology
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.