首页> 外文会议>Second joint biostatistics symposium (2012) >Measurement Issues of Some Widely Used Instruments May Mask Positive Findings in Medical Research -With Illustration of the Hamilton Rating Scale for Depression (HRSD) in a Large Clinical Trial for Major Depression
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Measurement Issues of Some Widely Used Instruments May Mask Positive Findings in Medical Research -With Illustration of the Hamilton Rating Scale for Depression (HRSD) in a Large Clinical Trial for Major Depression

机译:一些广泛使用的仪器的测量问题可能会掩盖医学研究的积极成果-举例说明在大型抑郁症临床试验中的汉密尔顿抑郁量表(HRSD)

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摘要

Recent years have seen increasing popularity of subjective patient-reported outcomes (PRO) such as depression and quality of life in medical research. These subjective outcomes needed to be measured by some instruments such as the Hamilton Rating Scale for Depression (HRSD) and the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). Unfortunately, most of instruments in medical research had not undergone rigorous development and evaluation stages (Teresi & Fleishman 2007). Here is an example. Many depression trials used the Hamilton Rating Scale for Depression (HRSD) as the primary measure of depression, although its severe measurement issues had been well-known. In a trail (Kellner, Kanpp, et al., 2006) that compares continuation electroconvulsive therapy (C-ECT) vs. Pharmacotherapy (C-Pharm) for relapse prevention in major depression, the total score of the 24-item HRSD (HRSD24) was used to define relapse or remission. That trail found no statistically significant differences between the two arms. However, the results might be misleading, simply because the unidimensional assumption made for the HRSD24, i.e., all of the 24 items are measuring a single domain (depression) and therefore the 24 item scores can be summed to a single total score as a measure of depression, might be wrong. In this study, the original data from that trial (201 patients with 98 C-ECT, 103 C-Pharm) were utilized to investigate measurement issues in the HRSD24. Confirmatory factor analyses (CFA) for the unidimensional assumption of the HRSD24 were implemented on the data at visits 1, 2, 3, and 4. Results showed that this uni-dimensional assumption failed at each of the 4 visits. This indicated that the single total HRSD24 score should not be used as the defining variable for relapse or remission at any of the visits, thus the analyses using this total score was misleading, and may conceal some true positive findings in that trial. Exploratory factor analysis (EFA) on all of the 24 items failed to yield a consistent factor structure across the 4 visits. Then item-level analyses of the 24 items were implemented, and results showed that statistically significant differences exist on 3 of the 24 items between the two arms. Clearly the measurement issues of the HRSD masked some positive findings in this large depression trial. Similarly, other widely used but not rigorously tested instruments may also masked some important findings in medical studies, especially those with negative findings.
机译:近年来,在医学研究中,诸如抑郁症和生活质量之类的主观患者报告结果(PRO)越来越受欢迎。这些主观结果需要通过汉密尔顿抑郁量表(HRSD)和医学成果研究36项简短健康调查(SF-36)等工具进行测量。不幸的是,医学研究中的大多数仪器都没有经过严格的开发和评估阶段(Teresi&Fleishman 2007)。这是一个例子。许多抑郁症试验均使用汉密尔顿抑郁量表(HRSD)作为抑郁症的主要指标,尽管众所周知,其严重的抑郁症问题也是如此。在比较持续电惊厥疗法(C-ECT)与药物疗法(C-Pharm)预防重度抑郁症复发的研究中(Kellner,Kanpp等人,2006),比较了24个项目的HRSD(HRSD24) )用于定义复发或缓解。那条线索发现两个部门之间没有统计学上的显着差异。但是,结果可能会产生误导,仅仅是因为针对HRSD24所做的一维假设,即所有24个项目都在测量一个域(抑郁),因此可以将这24个项目的分数求和为一个总分数抑郁症,可能是错误的。在这项研究中,该试验的原始数据(201名98 C-ECT患者,103 C-Pharm患者)被用于调查HRSD24中的测量问题。对HRSD24一维假设的验证性因子分析(CFA)已在访问1、2、3和4的数据上进行。结果表明,此一维假设在4次访问中均失败。这表明不应将单个HRSD24总得分用作任何就诊时复发或缓解的定义变量,因此,使用该总得分进行的分析具有误导性,并且可能掩盖了该试验中的某些真实阳性结果。对所有24个项目的探索性因素分析(EFA)未能在4次访问中产生一致的因素结构。然后对24个项目进行了项目级分析,结果表明,两个部门之间的24个项目中有3个存在统计学上的显着差异。显然,HRSD的测量问题掩盖了这项大型抑郁试验的一些积极发现。同样,其他广泛使用但未经严格测试的仪器也可能掩盖了医学研究中的一些重要发现,尤其是那些阴性结果。

著录项

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  • 会议地点 Beijing(CN)
  • 作者

    Chengwu Yang; Wenle Zhao;

  • 作者单位

    Assistant Professor of Biostatistics Department of Public Health Sciences College of Medicine, The Pennsylvania State University A210, ASB 3400H, 600 Centerview Drive, Hershey, PA 17033, USA;

    Research Associate Professor Division of Biostatistics and EpidemiologyMedical University of South Carolina, Charleston, South Carolina, USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
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