Mediation analysis is a statistical approach that can provide insights regarding the intermediary processes by which an intervention or exposure affects a given outcome. Mediation analyses rose to prominence, particularly in social science research, with the publication of the seminal paper by Baron and Kenny and is now commonly applied in many research disciplines, including health services research. Despite the growth in popularity, applied researchers may still encounter challenges in terms of conducting mediation analyses in practice. In this paper, we provide an overview of conceptual and methodological challenges that researchers face when conducting mediation analyses. Specifically, we discuss the following key challenges: (1) Conceptually differentiating mediators from other third variables, (2) Extending beyond the single mediator context, (3) Identifying appropriate datasets in which measurement and temporal ordering supports the hypothesized mediation model, (4) Selecting mediation effects that reflect the scientific question of interest, (5) Assessing the validity of underlying assumptions of no omitted confounders, (6) Addressing measurement error regarding the mediator, and (7) Clearly reporting results from mediation analyses. We discuss each challenge and highlight ways in which the applied researcher can approach these challenges.

翻译：中介分析是一种统计方法，可通过揭示干预或暴露因素影响特定结局的中间过程提供见解。自Baron和Kenny开创性论文发表以来，中介分析尤其在社会学研究中崭露头角，现已广泛应用于包括卫生服务研究在内的多个学科领域。尽管其应用日益普及，应用研究者在实际操作中仍可能遇到开展中介分析的相关挑战。本文系统梳理了研究者开展中介分析时面临的概念与方法论挑战，具体包括以下关键问题：(1) 概念上区分中介变量与其他第三方变量；(2) 超越单一中介变量情境；(3) 识别满足测量时序与假设中介模型匹配的合适数据集；(4) 选择能反映研究科学问题的中介效应指标；(5) 评估无遗漏混杂变量这一隐含假设的有效性；(6) 处理中介变量测量误差；(7) 清晰报告中介分析结果。针对每项挑战，本文阐述了应用研究者可采取的具体应对策略。