Robertson David S, Prevost A Toby, Bowden Jack
MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
Stat Med. 2015 Apr 15;34(8):1417-37. doi: 10.1002/sim.6413. Epub 2015 Feb 1.
When developing a new diagnostic test for a disease, there are often multiple candidate classifiers to choose from, and it is unclear if any will offer an improvement in performance compared with current technology. A two-stage design can be used to select a promising classifier (if one exists) in stage one for definitive validation in stage two. However, estimating the true properties of the chosen classifier is complicated by the first stage selection rules. In particular, the usual maximum likelihood estimator (MLE) that combines data from both stages will be biased high. Consequently, confidence intervals and p-values flowing from the MLE will also be incorrect. Building on the results of Pepe et al. (SIM 28:762-779), we derive the most efficient conditionally unbiased estimator and exact confidence intervals for a classifier's sensitivity in a two-stage design with arbitrary selection rules; the condition being that the trial proceeds to the validation stage. We apply our estimation strategy to data from a recent family history screening tool validation study by Walter et al. (BJGP 63:393-400) and are able to identify and successfully adjust for bias in the tool's estimated sensitivity to detect those at higher risk of breast cancer.
在开发一种针对某种疾病的新诊断测试时，通常有多个候选分类器可供选择，而且尚不清楚与现有技术相比，是否有任何一个能在性能上有所提升。可以采用两阶段设计，在第一阶段选择一个有前景的分类器（如果存在），以便在第二阶段进行最终验证。然而，由于第一阶段的选择规则，估计所选分类器的真实属性变得复杂。特别是，将两个阶段的数据结合起来的常用最大似然估计器（MLE）会有偏高的偏差。因此，从MLE得出的置信区间和p值也将是不正确的。基于Pepe等人（《统计医学》28:762 - 779）的研究结果，我们推导出了在具有任意选择规则的两阶段设计中，针对分类器敏感度的最有效条件无偏估计器和精确置信区间；条件是试验进入验证阶段。我们将我们的估计策略应用于Walter等人（《英国全科医学杂志》63:393 - 400）最近的一项家族病史筛查工具验证研究的数据，并且能够识别并成功校正该工具在估计检测乳腺癌高风险人群敏感度时的偏差。
