Table 1

Quantitative synthesis possibilities to address aspects of complexity

Aspect of complexity of interest	Examples of potential research question(s)	Synthesis possibilities	Further discussion
What ‘is’ the system? How can it be described?	What are the main influences on the health problem? How are they created and maintained? How do these influences interconnect?	Map the system, defining pathways and influences. Draw a logic model based on the key aspects for the research question at hand as a basis for thinking about the quantitative synthesis.	See companion paper,1 and section 2.1.
Interactions between components of complex interventions	What is the independent and combined effect of the individual components? How do the components work along and in combination to produce effects? (How do they interact to produce outcomes?)	Consider methods such as meta-regression, network meta-analysis and component-based approach that address intervention components, using models that allow investigation of interactions among components.	See sections 5.2 and 6.
Interactions of interventions with context and adaptation	Do the effects of the intervention appear to be context-dependent?	Consider subgroup analysis and meta-regression to examine how features of context impact on effect sizes.	See section 5.2.
System adaptivity (how does the system change?)	(How) does the system change when the intervention is introduced?	Identify behaviours or actions that might be affected, and consider these as outcomes in meta-analysis or meta-regression analyses. To account for correlations among them, multivariate methods might be considered.	See section 8.
	Which aspects of the system are affected? Does this potentiate or dampen its effects?	Identify units (eg, individuals or organisations) whose behaviour or actions might be affected, and consider these as outcomes in meta-analysis or meta-regression. Multilevel models might be appropriate to capture the different levels of impact, although may require access to individual participant data.	See sections 5.2 and 8.
Emergent properties	What are the effects (anticipated and unanticipated) which follow from this system change?	Identify other possible effects of the intervention, and consider these as outcomes in meta-analysis or meta-regression analyses. Consider model-driven meta-analysis or mathematical models (including simulation approaches) to investigate these further.	See section 8, box 2 and 3.
Non-linearity and phase changes	How do effects change over time?	Identify important time points and address these in separate meta-analyses, or using meta-regression analyses. Consider mathematical models to predict how effects might change over time.	See sections 5 and 8, and box 3.
Positive (reinforcing) and negative (balancing) feedback loops	What explains change in the effectiveness of the intervention over time?	Consider model-driven meta-analysis or mathematical models to investigate these.	See sections 7 and 8, boxes 2 and 3.
	Are the effects of an intervention dampened/suppressed by other aspects of the system (eg, contextual influences)?	Consider subgroup analysis and meta-regression to examine how features of the system impact on effect sizes.	See section 5.2.
Multiple (health and non-health) outcomes	What changes in processes and outcomes follow the introduction of this system change?	Identify behaviours or actions that might be affected, and consider these as outcomes in meta-analysis or meta-regression analyses. To account for correlations among them, multivariate methods might be considered. Consider meta-regression to examine the mediating effects of intermediate outcomes.	See section 8.
	At what levels in the system are they experienced?	Identify units (eg, individuals or organisations) whose behaviour or actions might be affected, and consider these as outcomes in meta-analysis or meta-regression. Multilevel models might be appropriate to capture the different levels of impact, although may require access to individual participant data.	See section 8.