Payne Mollie, Stringer Dominic, Carter Ben, Hardy Amy, Emsley Richard
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
BMC Med Res Methodol. 2025 May 16;25(1):135. doi: 10.1186/s12874-025-02585-3.
Understanding dose-response relationships is crucial in optimizing clinical outcomes, particularly in complex interventions such as psychotherapy. While dose-response research is common in pharmaceutical contexts, its application in complex interventions remains underexplored. This review examines existing statistical methods for modelling dose-response relationships in complex interventions, focusing on psychotherapy.
A systematic literature search following PRISMA guidelines identified studies proposing novel statistical methods or innovative applications of methods for analysing dose-response relationships. The search encompassed various databases, yielding 224 articles. After screening and exclusion, seven studies were eligible for analysis. Data synthesis categorized methods into three groups: multilevel and longitudinal modelling, non-parametric regression, and causal inference with instrumental variables. Additionally, a survey was conducted among clinical researchers to understand their perspectives on dosing decisions in psychotherapy trials.
Multilevel and longitudinal modelling techniques, although informative, were only applicable to participants with sessional data, limiting causal interpretations. Non-parametric regression methods provided avenues for causal inference but were constrained by assumptions. Causal inference with instrumental variables showed promise in addressing these limitations, particularly in randomised controlled trials, yet still require a priori assumption of the dose-response function. The results of our survey suggested that there is not sufficient information available to clinical researchers to make empirical dosing decisions in psychotherapeutic complex interventions.
This review highlights the scarcity of robust statistical methods for evaluating dose-response relationships in psychotherapy trials. The dose-response methodology applied to RCTs remains underdeveloped, hindering causal interpretations or requiring strong assumptions. Traditional approaches oversimplify outcomes, highlighting the need for more sophisticated methodologies. Clinical researchers emphasized the necessity for clearer guidelines and enhanced patient involvement in dosing decisions, echoing the broader findings of the review. Future research requires methodological advancements to inform effective decision-making in psychotherapy trials, ultimately optimizing patient care and outcomes.
了解剂量反应关系对于优化临床疗效至关重要，尤其是在心理治疗等复杂干预措施中。虽然剂量反应研究在药物领域很常见，但其在复杂干预措施中的应用仍未得到充分探索。本综述考察了用于对复杂干预措施（重点是心理治疗）中的剂量反应关系进行建模的现有统计方法。
按照PRISMA指南进行的系统文献检索，确定了提出用于分析剂量反应关系的新统计方法或方法创新应用的研究。检索涵盖了多个数据库，共获得224篇文章。经过筛选和排除，七项研究符合分析条件。数据综合将方法分为三组：多层次和纵向建模、非参数回归以及使用工具变量的因果推断。此外，还对临床研究人员进行了一项调查，以了解他们对心理治疗试验中剂量决策的看法。
多层次和纵向建模技术虽然提供了信息，但仅适用于有阶段数据的参与者，限制了因果解释。非参数回归方法为因果推断提供了途径，但受到假设的限制。使用工具变量的因果推断在解决这些限制方面显示出前景，特别是在随机对照试验中，但仍需要对剂量反应函数进行先验假设。我们的调查结果表明，临床研究人员没有足够的信息来在心理治疗复杂干预措施中做出基于经验的剂量决策。
本综述强调了在心理治疗试验中评估剂量反应关系的稳健统计方法的稀缺性。应用于随机对照试验的剂量反应方法仍不发达，阻碍了因果解释或需要很强的假设。传统方法过于简化结果，凸显了对更复杂方法的需求。临床研究人员强调需要更明确的指南以及加强患者在剂量决策中的参与，这与综述的更广泛发现相呼应。未来的研究需要方法上的进步，以为心理治疗试验中的有效决策提供依据，最终优化患者护理和治疗效果。
