Statistical methods for assessing diagnostic tests & estimating individualized probabilities of therapeutic benefit

评估诊断测试的统计方法

• 批准号: 8738-2007
• 负责人: Hanley, James
• 金额: $ 0.87万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362388
• 关键词: Statistical; methods; assessing; diagnostic; tests

I propose to develop statistical methods to (1) assess the performance of diagnostic tests and prognostic scoresand (2) estimate risk (cumulative incidence) functions and, from them, individualized 'what if' probabilities ofbenefit if a specific medical or lifestyle intervention is selected.In aim (1) the primary focus will be on the 'c' statistic derived from multiple logistic regression. The initialbiostatistical use of c was as the area under the roc curve (auc), to measure the discriminant ability of imagingtests (interpreted on a rating scale) and laboratory tests (measured on an interval scale). However, the index isalso standard output from the SAS (multiple) LOGISTIC procedure and is increasingly used to assess theability of diagnostic and prognostic scoring systems derived from a vector of predictors. If calculated from thesame data from which the model is fitted, the statistic overestimates the true performance of the system.Indeed, by construction, the c statistic calculated from PROC LOGISTIC cannot be less than the null 0.5.Several authors (from Cornfield and Lachenbruch several decades ago, to Copas, Rockette, and Pinskyrecently) have studied the factors that determine the magnitude of this bias. I propose to develop a simple'adjusted-c' statistic, similar to the adjusted-r-squared statistic. I expect that the needed attenuation/shrinkagewill be a function of the numbers of cases and non-cases, and the numbers of useful -- and useless -- candidatepredictor variables.A secondary focus will be on simplified lower confidence bound for the true auc when - in the simple imagingor laboratory test situations -- the observed auc is unity. Obuchowshi has provided limits for this situation, butunfortunately they are too distribution-specific to be of general use. My plan is to draw on the simplicity andclosed-form formulae based on overlapping exponential distributions, and on the insights on the var(auc)structure in our paper in Academic Radiology in 1997.Aim (2) has two parts. The first is to develop guidelines for a novel way (developed with my colleagueMiettinen) to fit smooth-in-time hazard functions to survival-type data, where the event E=1 represents anundesirable outcome. The purpose is to estimate cumulative incidence as a function of a vector of patientcharacteristics and lifestyle/medical management options. The approach is based on sampling theperson-moments; the main unknown is the choice of the sampling approach that gives the most stable estimateof the individualized cumulative incidence. The second aim is to derive an interval estimate for the probabilityof benefit within a time horizon T, for an individual with a personal profile vector x, and contemplatedmedical/behavioral Action (A=1) or not (A=0), i.e. the difference Prob[E=1 | x, T, A=0] - Prob[E=1 | x, T,A=1]. The hope is to have the individualized interval be test-based, so that it can be calculated from theinformation usually contained in study reports.We focus on individualized risk differences as a response to the inordinate emphasis on the 'average' patientand on hazard ratios rather than what matters to an individual: for individuals such as I, with profile x, what isthe difference in the probability of E over a time-horizon T if I choose one action over another? As an example,consider the 2005 NEJM report on an RCT which documented the extent to which radical prostatectomyreduces the risk of death from prostate cancer: the 'average' prostate cancer case-fatality rate within 10 yearswas 15% for those randomized to watchful waiting and 10% for those randomized to surgery; the hazard ratiowas 0.55. The report contained no useful information for men with a patient / tumour profile (age at diagnosis,Gleason score, pre-treatment PSA level) that was more/less favourable than the 'average' profile to which thesummary results presumably apply. With methods that are aimed at individualized risk, and that do not rely onthe non-smooth estimates obtained from Cox's proportional hazards model, we plan to change thecontemporary culture of statistical reporting to be more responsive to individual 'clients'.

我建议开发统计方法来（1）评估诊断测试和预后评分的表现，（2）估计风险（累积发生率）函数，并根据它们，如果选择特定的医疗或生活方式干预，则个体化的“假设”获益概率。在目标（1）中，主要关注点是从多元逻辑回归得出的“c”统计量。 c 最初的生物统计学用途是 roc 曲线下面积 (auc)，用于测量成像测试（按评级量表解释）和实验室测试（按间隔量表测量）的判别能力。然而，该指数也是 SAS（多重）LOGISTIC 程序的标准输出，并且越来越多地用于评估源自预测变量向量的诊断和预后评分系统的能力。如果根据拟合模型的相同数据进行计算，则统计量会高估系统的真实性能。事实上，通过构造，从 PROC LOGISTIC 计算出的 c 统计量不能小于零 0.5。几位作者（从几十年前的 Cornfield 和 Lachenbruch，到最近的 Copas、Rockette 和 Pinsky）研究了决定这种偏差大小的因素。我建议开发一个简单的“调整 c”统计量，类似于调整 r 平方统计量。我预计所需的衰减/收缩将是病例数和非病例数以及有用和无用候选预测变量的数量的函数。第二个重点将是在简单成像或实验室测试情况下观察到的 AUC 为统一时真实 AUC 的简化置信下限。 Obuchowshi 为这种情况提供了限制，但不幸的是，它们过于特定于发行版，无法普遍使用。我的计划是利用基于重叠指数分布的简单封闭式公式，以及 1997 年学术放射学论文中对 var(auc) 结构的见解。目标 (2) 有两个部分。第一个是制定一种新方法的指南（与我的同事 Miettinen 一起开发），以将时间平滑风险函数拟合到生存类型数据，其中事件 E=1 代表不良结果。目的是估计累积发病率作为患者特征和生活方式/医疗管理选项向量的函数。该方法基于对人物时刻进行采样；主要的未知因素是采样方法的选择，该方法可以对个体累积发生率进行最稳定的估计。第二个目标是对于具有个人概况向量 x 和预期医疗/行为行动 (A=1) 或不 (A=0) 的个人，得出时间范围 T 内受益概率的区间估计，即差值 Prob[E=1 | x, T, A=0] - 概率[E=1 | x, T, A=0] - 概率[E=1 | x, T, A=0] x，T，A=1]。希望个体化间隔是基于测试的，以便可以根据研究报告中通常包含的信息进行计算。我们关注个体化风险差异，作为对“平均”患者和风险比的过分强调的回应，而不是对个体重要的事情：对于像我这样的具有x特征的个体，如果我选择一种行动而不是另一种行动，则E在时间范围T内的概率有什么差异？例如，2005 年 NEJM 的随机对照试验报告记录了根治性前列腺切除术降低前列腺癌死亡风险的程度：10 年内“平均”前列腺癌病死率对于随机接受观察等待的患者为 15%，而对于随机接受手术的患者为 10%；风险比为0.55。对于患者/肿瘤概况（诊断时的年龄、格里森评分、治疗前 PSA 水平）的男性，该报告没有包含比总结结果可能适用的“平均”概况更有利/更不利的有用信息。通过针对个体化风险且不依赖于从考克斯比例风险模型获得的非平滑估计的方法，我们计划改变当代的统计报告文化，以更好地响应个体“客户”。