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Reconciling disparate data to determine the right answer: A grounded theory of meta analysts' reasoning in metaanalysis

机译:调和不同的数据以确定正确的答案:荟萃分析中元分析师推理的扎实理论

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摘要

While the systematic review process is intended to maximize objectivity and limit researchers' biases, examples remain of discordant recommendations from meta-analyses. Current guidelines to explore discrepancies assume the variation is produced by methodological differences and thus focus only on the study process. Because heterogeneity of interpretation also occurs when experts examine the same data, our purpose was to examine if there are reasoning differences, ie, in how information is processed and valued. We created simulated meta-analyses based on idealized randomized studies (ie, perfect studies with no bias) to ensure differences in interpretations could only be due to reasoning. We recruited published meta-analysts using purposeful variables. We conducted 3 audio-recorded interviews per participant using structured and semi-structured interviews, with paraphrasing and reflective listening to enhance and verify responses. Recruitment and analysis of transcripts and field notes followed the principles of grounded theory (eg, theoretical saturation, constant comparative analysis). Results show the complexity of meta-analytic reasoning. At each step of the process, participants attempted to reconcile disparate forms of knowledge to determine a right answer (moral concern) and accurately draw a treatment effect (epistemological concern). The reasoning processes often shifted between considering the meta-analysis as if the data were whole, and as if the data were discrete components (individual studies). These findings highlight paradigmatic tensions regarding the epistemological premises of meta-analysis, resembling previous historical investigations of the functioning of scientific communities. In understanding why different meta-analysts interpret data differently, it may be unrealistic to expect objective homogenous recommendations based on meta-analyses.
机译:尽管系统的审查过程旨在最大程度地提高客观性并限制研究人员的偏见,但仍存在一些荟萃分析中不一致建议的实例。当前探讨差异的指南假设差异是由方法上的差异引起的,因此仅关注研究过程。因为当专家检查相同的数据时,解释的异质性也会发生,因此我们的目的是检查是否存在推理差异,即信息的处理和价值方式。我们基于理想化的随机研究(即无偏差的完美研究)创建了模拟的荟萃分析,以确保解释上的差异只能归因于推理。我们使用有目的的变量招募了已发表的荟萃分析。我们使用结构化和半结构化访谈对每个参与者进行了3场录音访谈,并通过措辞和反思性聆听来增强和验证响应。笔录和现场笔记的招募和分析遵循扎根理论的原则(例如,理论饱和度,持续的比较分析)。结果显示了元分析推理的复杂性。在过程的每个步骤中,参与者都试图调和不同形式的知识,以确定正确的答案(道德问题)并准确得出治疗效果(病因问题)。推理过程通常会在考虑荟萃分析(好像数据是完整的)和好像数据是离散的成分(个体研究)之间切换。这些发现凸显了关于元分析的认识论前提的范式张力,类似于先前对科学共同体功能的历史调查。在理解为什么不同的荟萃分析者对数据的理解不同时,期望基于荟萃分析的客观一致的建议可能是不现实的。

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  • 来源
    《Research Synthesis Methods》 |2018年第1期|25-40|共16页
  • 作者单位

    Ingram School of Nursing, McGill University, Montreal, Canada;

    Division of Oral Health and Society Faculty of Dentistry, McGill University, Montreal, Canada;

    Ingram School of Nursing, McGill University, Montreal, Canada;

    Department of Mathematics and Statistics, McGill University, Montreal, Canada;

    Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada;

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