# Reviewing methodological approaches to dose-response modelling in complex interventions: insights and perspectives

## TL;DR

This review explores statistical methods for understanding dose-response relationships in psychotherapy, highlighting gaps and the need for better approaches to improve clinical outcomes.

## Contribution

The paper provides a systematic review of dose-response methodologies in psychotherapy, emphasizing their limitations and the need for methodological advancements.

## Key findings

- Multilevel and longitudinal models are limited to sessional data and lack causal interpretation.
- Non-parametric regression and instrumental variables offer promise but require strong assumptions.
- Clinical researchers lack sufficient information for empirical dosing decisions in psychotherapy.

## Abstract

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.

The online version contains supplementary material available at 10.1186/s12874-025-02585-3.