The 2009 FDA PRO guidance, Potential Type I error, Descriptive Statistics and Pragmatic estimation of the number of interviews for item elicitation.

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Josh Fleckner, Chris Barker
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引用次数: 0

Abstract

A statistical methodology named "capture recapture", a Kaplan-Meier Summary Statistic, and an urn model framework are presented to describe the elicitation, then estimate both the number of interviews and the total number of items ("codes") that will be elicited during patient interviews, and present a summary graphical statistic that "saturation" has occurred. This methodology is developed to address a gap in the FDA 2009 PRO and 2012 PFDD guidance for determining the number of interviews (sample size). This estimate of the number of interviews (sample size) uses a two-step procedure. The estimate of the total number of items is then used to estimate the number of interviews to elicit all items. A framework called an urn model is a framework for describing the elicitation and demonstrate the algorithm for declaring saturation "first interview with zero new codes". A caveat emptor is that due to independence assumptions, the urn model is not used as a method for estimating probabilities. The URN model provides a framework to demonstrate that an algorithm such as "first interview with zero new codes" may establish that all codes have been elicited. The limitations of the Urn model, capture recapture, and Kaplan-Meier are summarized. The statistical methods and the estimates supplement but do not replace expert judgement and declaration of "saturation." A graphical summary statistic is presented to summarize "saturation," after expert declaration for two algorithms. An example of a capture-recapture estimate, using simulated data is provided. The example suggests that the estimate of total number of codes may be accurate when prepared as early as the second interview. A second simulation is presented with an URN model, under a strong assumption of independence that an algorithm such as 'first interview with zero new codes" may fail to identify all codes. Potential errors in declaration of saturation are presented. Recommendations are presented for additional research and the use of the algorithm "first interview with zero new codes."

2009 年 FDA PRO 指南、潜在的 I 类错误、描述性统计和项目征询访谈次数的实用估算。
本文介绍了一种名为 "捕获再捕获 "的统计方法、Kaplan-Meier 统计摘要和瓮模型框架,用于描述诱导过程,然后估算访谈次数和患者访谈期间将诱导出的项目("代码")总数,并以图形统计摘要的方式说明 "饱和 "已经发生。此方法的开发是为了弥补 FDA 2009 PRO 和 2012 PFDD 指南在确定访谈次数(样本大小)方面的不足。对访谈次数(样本量)的估算采用两步程序。首先估算项目总数,然后使用估算的项目总数估算获取所有项目的访谈次数。一个称为 "urn 模型 "的框架可用于描述诱导过程,并演示宣布 "首次访谈无新代码 "为饱和的算法。需要注意的是,由于存在独立性假设,瓮模型不能用作估计概率的方法。瓮模型提供了一个框架,可以证明 "首次访谈无新代码 "这样的算法可以确定所有代码都已引出。本文总结了瓮模型、捕获再捕获和 Kaplan-Meier 的局限性。统计方法和估算结果是对专家判断和 "饱和 "声明的补充,但不能取代专家判断和 "饱和 "声明。在专家宣布两种算法的 "饱和度 "后,提出了一个图解统计摘要。提供了一个使用模拟数据进行捕获-再捕获估算的例子。该示例表明,如果早在第二次访谈时就做好准备,对代码总数的估计可能是准确的。在 "第一次访谈无新代码 "等算法可能无法识别所有代码的独立性假设下,使用 URN 模型进行了第二次模拟。还介绍了在宣布饱和时可能出现的错误。提出了关于进一步研究和使用 "首次访谈零新代码 "算法的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
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