Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data

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dc.contributor.authorMoreno-Betancur, M.
dc.contributor.authorLynch, J.W.
dc.contributor.authorPilkington, R.M.
dc.contributor.authorSchuch, H.S.
dc.contributor.authorGialamas, A.
dc.contributor.authorSawyer, M.G.
dc.contributor.authorChittleborough, C.R.
dc.contributor.authorSchurer, S.
dc.contributor.authorGurrin, L.C.
dc.date.issued2023
dc.descriptionAdvance Access Publication Date: 18 May 2022
dc.description.abstractBackground: Populations willing to participate in randomized trials may not correspond well to policy-relevant target populations. Evidence of effectiveness that is complementary to randomized trials may be obtained by combining the ‘target trial’ causal inference framework with whole-of-population linked administrative data. Methods: We demonstrate this approach in an evaluation of the South Australian Family Home Visiting Program, a nurse home visiting programme targeting socially disadvantaged families. Using de-identified data from 2004–10 in the ethics-approved Better Evidence Better Outcomes Linked Data (BEBOLD) platform, we characterized the policy-relevant population and emulated a trial evaluating effects on child developmental vulnerability at 5years (n¼4160) and academic achievement at 9 years (n¼6370). Linkage to seven health, welfare and education data sources allowed adjustment for 29 confounders using Targeted Maximum Likelihood Estimation (TMLE) with SuperLearner. Sensitivity analyses assessed robustness to analytical choices. Results: We demonstrated how the target trial framework may be used with linked administrative data to generate evidence for an intervention as it is delivered in practice in the community in the policy-relevant target population, and considering effects on VC outcomes years down the track. The target trial lens also aided in understanding and limiting the increased measurement, confounding and selection bias risks arising with such data. Substantively, we did not find robust evidence of a meaningful beneficial intervention effect. Conclusions: This approach could be a valuable avenue for generating high-quality, policy-relevant evidence that is complementary to trials, particularly when the target populations are multiply disadvantaged and less likely to participate in trials.
dc.description.statementofresponsibilityMargarita Moreno-Betancur, JohnW. Lynch, Rhiannon M. Pilkington, Helena S. Schuch, Angela Gialamas, Michael G. Sawyer, Catherine R. Chittleborough, Stefanie Schurer, and Lyle C. Gurrin
dc.identifier.citationInternational Journal of Epidemiology, 2023; 52(1):119-131
dc.identifier.doi10.1093/ije/dyac092
dc.identifier.issn0300-5771
dc.identifier.issn1464-3685
dc.identifier.orcidLynch, J.W. [0000-0003-2781-7902]
dc.identifier.orcidPilkington, R.M. [0000-0001-6974-8496]
dc.identifier.orcidSchuch, H.S. [0000-0001-9932-9698]
dc.identifier.orcidGialamas, A. [0000-0001-7440-8154]
dc.identifier.orcidSawyer, M.G. [0000-0002-7834-0561]
dc.identifier.orcidChittleborough, C.R. [0000-0003-3196-7137]
dc.identifier.urihttps://hdl.handle.net/2440/135177
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1099422
dc.relation.granthttp://purl.org/au-research/grants/arc/DE190101326
dc.rights© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.source.urihttps://doi.org/10.1093/ije/dyac092
dc.subjectCausal inference
dc.subjectgeneralizability
dc.subjectlinked data
dc.subjectnurse visiting programme
dc.subjectsocial disadvantage
dc.subjecttarget trial
dc.subjecttargeted maximum likelihood estimation
dc.subjecttransportability
dc.titleEmulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
dc.typeJournal article
pubs.publication-statusPublished

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