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. |