An Algorithm to Evaluate Methodological Rigor and Risk of Bias in Single-Case Studies.

IF 2 3区心理学 Q3 PSYCHOLOGY, CLINICAL Behavior Modification Pub Date : 2023-11-01 Epub Date: 2019-08-30 DOI:10.1177/0145445519863035

Michael Perdices, Robyn L Tate, Ulrike Rosenkoetter

引用次数: 20

Abstract

Critical appraisal scales play an important role in evaluating methodological rigor (MR) of between-groups and single-case designs (SCDs). For intervention research this forms an essential basis for ascertaining the strength of evidence. Yet, few such scales provide classifications that take into account the differential weighting of items contributing to internal validity. This study aimed to develop an algorithm derived from the Risk of Bias in N-of-1 Trials (RoBiNT) Scale to classify MR and risk of bias magnitude in SCDs. The algorithm was applied to 46 SCD experiments. Two experiments (4%) were classified as Very High MR, 14 (30%) as High, 5 (11%) as Moderate, 2 (4%) as Fair, 2 (4%) as Low, and 21 (46%) as Very Low. These proportions were comparable to the What Works Clearinghouse classifications: 13 (28%) met standards, 8 (17%) met standards with reservations, and 25 (54%) did not meet standards. There was strong association between the two classification systems.

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单例研究中评估方法严谨性和偏倚风险的算法。

临界评估量表在评估组间和单病例设计（SCD）的方法严谨性（MR）方面发挥着重要作用。对于干预研究来说，这是确定证据强度的重要基础。然而，很少有这样的量表提供考虑到对内部有效性有贡献的项目的不同权重的分类。本研究旨在开发一种源自1次试验中N次偏倚风险量表（RoBiNT）的算法，对SCD中的MR和偏倚程度风险进行分类。该算法已应用于46个SCD实验。两个实验（4%）被归类为甚高MR，14个（30%）被归类于高MR，5个（11%）被分类为中等MR，2个（4%）为一般MR，2（4%）归类于低MR，21个（46%）归类于甚低MR。这些比例与What Works Clearinghouse的分类相当：13个（28%）符合标准，8个（17%）符合保留标准，25个（54%）不符合标准。这两个分类系统之间有很强的联系。

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

Behavior Modification PSYCHOLOGY, CLINICAL-

CiteScore

5.30

自引率

0.00%

发文量

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