模拟生物标志物富集对阿尔茨海默病预防试验的影响：概念框架与示例
Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: conceptual framework and example.
作者信息
Leoutsakos Jeannie-Marie S, Bartlett Alexandra L, Forrester Sarah N, Lyketsos Constantine G
机构信息
Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
STEM Magnet Program, South River High School, Edgewater, MD, USA.
出版信息
Alzheimers Dement. 2014 Mar;10(2):152-61. doi: 10.1016/j.jalz.2013.05.1776. Epub 2013 Aug 15.
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.
背景
我们提出了一个用于模拟的概念框架，以确定生物标志物富集在未来阿尔茨海默病预防试验中提高检测治疗效果统计功效的效用。我们纳入了一组有限的模拟结果来说明该框架的各个方面。
方法
我们基于阿尔茨海默病抗炎预防试验模拟数据，并涵盖了一系列样本量、生物标志物阳性预测值和治疗效果。我们还研究了假设参数估计的同质性作为痴呆症结果函数的后果。
结果
使用生物标志物将在无干预情况下患阿尔茨海默病的样本比例从0.5提高到0.8，当n = 200时，功效将从0.35提高到0.69。忽略样本异质性会导致对功效的高估。
结论
生物标志物富集可提高统计功效，但预期提高幅度的估计对框架中概述的各种假设敏感。
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