Making predictions under hypothetical interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care

Abstract Background Most existing clinical prediction models do not allow predictions under hypothetical interventions. Such predictions allow predicted risk under different proposed strategies to be compared, which is useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three hypothetical interventions: smoking cessation, reducing blood pressure, and reducing cholesterol. Methods We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under hypothetical interventions: (a) conditioning on hypothetical interventions in non-causal models; (b) integrating existing prediction models with causal effects estimated using observational causal inference methods; and (c) integrating existing prediction models with causal effects reported in published literature. Results The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between 2.4% and 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.1–4.5%. Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 1.9% and 2.8%. Conclusions Estimated absolute risk reductions based on non-causal methods were very different to those based on causal methods, and there was also substantial variation in estimates within the causal methods. Researchers wishing to estimate hypothetical risk should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.