中华医学教育杂志 ›› 2025, Vol. 45 ›› Issue (11): 822-826.DOI: 10.3760/cma.j.cn115259-20241231-01381

• 课程改革与建设 • 上一篇    下一篇

流行病学偏倚分析技术课程的建设与探索

丁盈盈1, 余勇夫2, 郑英杰1   

  1. 1复旦大学公共卫生学院流行病学教研室,教育部公共卫生安全重点实验室,上海 200032;
    2复旦大学公共卫生学院卫生统计学教研室,上海 200032
  • 收稿日期:2024-12-31 发布日期:2025-10-30
  • 通讯作者: 郑英杰, Email: yjzheng@fudan.edu.cn
  • 基金资助:
    上海市自然科学基金(21ZR1403800)

Curriculum development and exploration of epidemiological bias analysis techniques

Ding Yingying1, Yu Yongfu2, Zheng Yingjie1   

  1. 1Department of Epidemiology, School of Public Health, and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China;
    2Department of Biostatistics, School of Public Health Fudan University, Shanghai 200032, China
  • Received:2024-12-31 Published:2025-10-30
  • Contact: Zheng Yingjie, Email: yjzheng@fudan.edu.cn
  • Supported by:
    Natural Science Foundation of Shanghai(21ZR1403800)

摘要: 定量偏倚分析能够有效估计潜在偏倚大小和方向,帮助研究人员评估结果的稳健性。为了提升学生专业能力,自2013年起,复旦大学公共卫生学院面向医学相关专业的硕士研究生和博士研究生开设流行病学偏倚分析技术课程,融合理论讲授、案例分析与计算机操作,旨在将理论知识与实际应用有机结合。2020至2024年75份学生汇报幻灯片和Take-home试卷分析结果显示,大部分学生能够准确识别偏倚、构建因果图并合理运用定量偏倚分析方法,考核成绩与学生的流行病学及卫生统计学专业背景密切相关。2024年匿名教学效果问卷调查结果(共回收26份有效问卷)显示,学生对课程的满意度评分为(9.0±1.3)分,理论掌握自评分为(8.3±1.4)分,技术运用自评分为(7.8±1.7)分,10名(38.4%)学生计划将偏倚分析纳入课题或论文。课程在提升学生偏倚分析能力方面取得积极效果,但技术运用仍需加强。未来将增加实践比重、优化教学内容,以持续提升教学质量。

关键词: 流行病学, 定量偏倚分析, 课程建设, 研究生教育

Abstract: Quantitative bias analysis can accurately estimate the magnitude and direction of potential biases, thereby helping researchers assess the robustness of findings against potential biases. To enhance students' professional competencies, the School of Public Health in Fudan University has developed a course on epidemiological bias analysis techniques since 2013 to master's and doctoral students in medical-related disciplines. The curriculum integrates theoretical lectures, case studies, and computer-based operations to combine theoretical knowledge with practical application. Analysis of 75 student presentations and take-home exams from 2020 to 2024 indicated that most students could accurately identify bias types, construct causal diagrams, and appropriately apply quantitative bias analysis methods. Examination performance was strongly associated with students' backgrounds in epidemiology and biostatistics. Results from an anonymous online teaching evaluation survey conducted in 2024 (26 valid responses) indicated overall satisfaction score of 9.0±1.3, self-rated theoretical mastery score of 8.3±1.4, self-rated technical application score of 7.8±1.7, and 10 students (38.4%) reported plans to incorporate bias analysis into their research projects or theses. This course has demonstrated positive outcomes in improving students' abilities in bias analysis, though practical application skills warrant further strengthening. Future course development will increase practical exercises and refine course content to continuously improve teaching quality.

Key words: Epidemiology, Quantitative bias analysis, Curriculum construction, Graduate education

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