Protecting against researcher bias in secondary data analysis: challenges and potential solutions.

Protecting against researcher bias in secondary data analysis: challenges and potential solutions.
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DOI:
10.1007/s10654-021-00839-0
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发表时间:
2022-01
影响因子:
13.6
通讯作者:
Munafò MR
Munafò MR
中科院分区:
医学1区
文献类型:
--
作者:
Baldwin JR;Pingault JB;Schoeler T;Sallis HM;Munafò MR

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对次级数据源(如队列研究、调查数据和行政记录)的分析有可能为科学和社会最紧迫的问题提供答案。然而,研究人员的偏见可能会导致二手数据分析中有问题的研究实践,这可能会扭曲证据基础。虽然预先登记有助于防止研究人员的偏见,但它对二级数据分析提出了挑战。在本文中,我们描述了这些挑战,并提出了新的解决方案和替代方法。建议的解决方案包括以下方法:(1)解决与数据先验知识相关的偏见,(2)实现非假设驱动研究的预注册,(3)帮助确保预注册分析适用于数据,以及(4)解决预注册中分析灵活性降低所带来的困难。对于每个解决方案,我们都为研究人员和数据监护人提供实施指导。采用这些做法有助于防止研究人员在二级数据分析中的偏见,提高基于现有数据的研究的稳健性。
Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society’s most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.
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