存在协变量时最小有效剂量和峰值剂量的自适应等张估计
Adaptive Isotonic Estimation of the Minimum Effective and Peak Doses in the Presence of Covariates.
作者信息
Xiao Changfu, Ivanova Anastasia
机构信息
Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, USA.
出版信息
J Stat Plan Inference. 2012 Jul 1;142(7):1899-1907. doi: 10.1016/j.jspi.2012.01.024.
We consider a problem of estimating the minimum effective and peak doses in the presence of covariates. We propose a sequential strategy for subject assignment that includes an adaptive randomization component to balance the allocation to placebo and active doses with respect to covariates. We conclude that either adjusting for covariates in the model or balancing allocation with respect to covariates is required to avoid bias in the target dose estimation. We also compute optimal allocation to estimate the minimum effective and peak doses in discrete dose space using isotonic regression.
我们考虑在存在协变量的情况下估计最小有效剂量和峰值剂量的问题。我们提出了一种受试者分配的序贯策略，该策略包括一个自适应随机化组件，以在协变量方面平衡安慰剂和活性剂量的分配。我们得出结论，为避免目标剂量估计中的偏差，需要在模型中对协变量进行调整或在协变量方面平衡分配。我们还使用保序回归计算了在离散剂量空间中估计最小有效剂量和峰值剂量的最优分配。
Zotero 插件