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Cost-efficient designs based on linearly associated biomarkers

机译：基于线性相关生物标记物的经济高效设计

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摘要

A major limiting factor in much of the epidemiological and environmental researches is the cost of measuring the biomarkers or analytes of interest. Often, the number of specimens available for analysis is greater than the number of assays that is budgeted for. These assays are then performed on a random sample of specimens. Regression calibration is then utilized to infer biomarker levels of expensive assays from other correlated biomarkers that are relatively inexpensive to obtain and analyze. In other contexts, use of pooled specimens has been shown to increase efficiency in estimation. In this article, we examine two types of pooling in lieu of a random sample. The first is random (or traditional) pooling, and we characterize the second as "optimal" pooling. The second, which we propose for regression analysis, is pooling based on specimens ranked on the less expensive biomarker. The more expensive assay is then performed on the pool of relatively similar measurements. The optimal nature of this technique is also exemplified via Monte Carlo evaluations and real biomarker data. By displaying the considerable robustness of our method via a Monte Carlo study, it is shown that the proposed pooling design is a viable option whenever expensive assays are considered.
机译：在许多流行病学和环境研究中，主要的限制因素是测量目标生物标志物或分析物的成本。通常，可用于分析的标本数量大于预算的分析数量。然后对随机样本样品进行这些测定。然后利用回归校准从其他相关生物标志物推断出昂贵的测定法的生物标志物水平，这些相关生物标志物的获取和分析相对便宜。在其他情况下，已证明使用合并标本可以提高估计效率。在本文中，我们将检查两种类型的合并以代替随机样本。第一种是随机（或传统）池，第二种是“最佳”池。我们建议进行回归分析的第二种方法是基于在较便宜的生物标记物上排名的标本进行合并。然后，在相对相似的测量结果池中执行更昂贵的分析。该技术的最佳性质还通过蒙特卡洛评估和真实的生物标记数据得到例证。通过蒙特卡洛研究显示我们的方法具有相当强的鲁棒性，表明无论何时考虑进行昂贵的测定，建议的合并设计都是可行的选择。

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