Advances and Challenges in the Research of Integration Methods of Animal Experimental Evidence

doi:10.12300/j.issn.1674-5817.2024.079

Abstract

Abstract:

Integrating evidence from animal experiments is a critical component of biomedical research, providing essential prior information for in-depth investigations of disease mechanisms and new drug development. Animal models have played an irreplaceable role in simulating human diseases. However, the integration of evidence from animal experiments has faced numerous challenges, including insufficient emphasis, significant heterogeneity in study designs, high publication bias, and discrepancies with clinical research practices. This paper first identifies existing issues in the original research evidence from animal experiments, such as the selection and applicability of animal models, considerations in the design of experimental studies, and factors influencing the translation of animal experimental evidence. It then discusses various methods for integrating this evidence, including systematic review and meta-analysis, overview of systematic review/umbrella review, scoping review, and evidence mapping, while highlighting recent advancements in their application. Finally, the paper addresses the main challenges currently encountered in the integration of evidence from animal experiments and proposes targeted improvement strategies aimed at enhancing the efficiency of translating research outcomes into clinical practice and promoting the advancement of evidence-based medicine. By continuously optimizing original experimental research protocols and evidence integration practices, this work aims to establish a more efficient and scientific environment for the synthesis of evidence from animal experiments, ultimately contributing to clinical trials and human health.

Key words: Animal experiments, Evidence integration, Research progress, Evidence-based medicine

CLC Number:

Q95-33

ZHENG Qingyong,LI Tengfei,XU Jianguo,et al. Advances and Challenges in the Research of Integration Methods of Animal Experimental Evidence[J]. Laboratory Animal and Comparative Medicine, 2024, 44(5): 567-576. DOI: 10.12300/j.issn.1674-5817.2024.079.

Figures/Tables 3

References 54

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整合方法 Integration methods	应用特点 Application characteristics	实例参考 Examples
系统评价与Meta分析 Systematic review and meta-analysis	通过严谨的研究设计和方法学，对现有的动物实验数据进行系统性综述与定量分析（部分），以提供最高级别的证据。Meta分析可综合多个研究的结果，对其进行定量整合，揭示干预措施的总体效果	Perna A, et al/1996^[24]
单臂试验Meta分析 Meta-analysis of single-arm trials	通常用于关注单个群体中事件的发生率，特别适用于处理稀有事件或样本量较小的研究	Alonso S, et al/2016^[25]
累积Meta分析 Cumulative meta-analysis	将研究资料作为连续性的统一体，评估随时间累积研究结果的动态变化趋势	Inchuai R, et al/2021^[26]
定性系统评价/Meta整合 Qualitative systematic review/meta-synthesis	关注研究结果的叙述性合成，适用于研究设计差异过大或数据类型不适合定量分析的情境	Pommergaard H C, et al/2011^[27]
快速系统评价 Rapid review	高效、加速版的系统评价方法，适用于紧急决策或时间敏感情况下的证据综合	Ciapponi A, et al/2011^[28]
网状Meta分析 Network meta-analysis	结合直接和间接证据，综合多个比较组的结果，评估不同干预措施之间的相对效果	Yao M, et al /2015^[29]
系统评价再评价/伞形综述 Overview of systematic reviews/ umbrella review	对已有的多个系统评价进行再评价，部分会再次进行统计分析，提供某一领域的全面证据概览	Hirst J A, et al /2014^[30]
范围综述 Scoping review	映射某一研究领域的文献分布，识别研究的数量和特征，揭示知识空白，通常不进行质量评估和定量分析	Gardner E G, et al /2019^[31]
证据图谱 Evidence mapping	主要包括证据图与证据差距图，其系统性地检索和分析现有证据，通过表格或图形形式展示研究现状和证据差距，支持决策制定和研究规划	Koch S E, et al /2022^[32]; moola S, et al /2021^[33]

整合方法 Integration methods	应用特点 Application characteristics	实例参考 Examples
系统评价与Meta分析 Systematic review and meta-analysis	通过严谨的研究设计和方法学，对现有的动物实验数据进行系统性综述与定量分析（部分），以提供最高级别的证据。Meta分析可综合多个研究的结果，对其进行定量整合，揭示干预措施的总体效果	Perna A, et al/1996^[24]
单臂试验Meta分析 Meta-analysis of single-arm trials	通常用于关注单个群体中事件的发生率，特别适用于处理稀有事件或样本量较小的研究	Alonso S, et al/2016^[25]
累积Meta分析 Cumulative meta-analysis	将研究资料作为连续性的统一体，评估随时间累积研究结果的动态变化趋势	Inchuai R, et al/2021^[26]
定性系统评价/Meta整合 Qualitative systematic review/meta-synthesis	关注研究结果的叙述性合成，适用于研究设计差异过大或数据类型不适合定量分析的情境	Pommergaard H C, et al/2011^[27]
快速系统评价 Rapid review	高效、加速版的系统评价方法，适用于紧急决策或时间敏感情况下的证据综合	Ciapponi A, et al/2011^[28]
网状Meta分析 Network meta-analysis	结合直接和间接证据，综合多个比较组的结果，评估不同干预措施之间的相对效果	Yao M, et al /2015^[29]
系统评价再评价/伞形综述 Overview of systematic reviews/ umbrella review	对已有的多个系统评价进行再评价，部分会再次进行统计分析，提供某一领域的全面证据概览	Hirst J A, et al /2014^[30]
范围综述 Scoping review	映射某一研究领域的文献分布，识别研究的数量和特征，揭示知识空白，通常不进行质量评估和定量分析	Gardner E G, et al /2019^[31]
证据图谱 Evidence mapping	主要包括证据图与证据差距图，其系统性地检索和分析现有证据，通过表格或图形形式展示研究现状和证据差距，支持决策制定和研究规划	Koch S E, et al /2022^[32]; moola S, et al /2021^[33]

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