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

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dc.citation.volume4
dc.contributor.authorLin L
dc.contributor.authorPoppe K
dc.contributor.authorWood A
dc.contributor.authorMartin GP
dc.contributor.authorPeek N
dc.contributor.authorSperrin M
dc.contributor.editorPiccininni M
dc.date.accessioned2024-11-06T01:51:57Z
dc.date.available2024-11-06T01:51:57Z
dc.date.issued2024-04-03
dc.description.abstractBackground: Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three 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 intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining 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 a non-causal estimate of 2.5% and a causal estimate of 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 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate). Conclusions: Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.
dc.description.confidentialfalse
dc.identifier.citationLin L, Poppe K, Wood A, Martin GP, Peek N, Sperrin M. (2024). Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care. Frontiers in Epidemiology. 4.
dc.identifier.doi10.3389/fepid.2024.1326306
dc.identifier.eissn2674-1199
dc.identifier.elements-typejournal-article
dc.identifier.number1326306
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71930
dc.languageEnglish
dc.publisherFrontiers Media S.A.
dc.publisher.urihttps://www.frontiersin.org/journals/epidemiology/articles/10.3389/fepid.2024.1326306/full
dc.relation.isPartOfFrontiers in Epidemiology
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclinical prediction model
dc.subjectcausal inference
dc.subjectcardiovascular diseases
dc.subjectprevention
dc.subjecttreatment
dc.titleMaking predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care
dc.typeJournal article
pubs.elements-id491744
pubs.organisational-groupCollege of Health
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