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

BACKGROUND: 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...

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Autores principales: Moreno-Betancur, Margarita, Lynch, John W, Pilkington, Rhiannon M, Schuch, Helena S, Gialamas, Angela, Sawyer, Michael G, Chittleborough, Catherine R, Schurer, Stefanie, Gurrin, Lyle C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908050/
https://www.ncbi.nlm.nih.gov/pubmed/35588223
http://dx.doi.org/10.1093/ije/dyac092
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author Moreno-Betancur, Margarita
Lynch, John W
Pilkington, Rhiannon M
Schuch, Helena S
Gialamas, Angela
Sawyer, Michael G
Chittleborough, Catherine R
Schurer, Stefanie
Gurrin, Lyle C
author_facet Moreno-Betancur, Margarita
Lynch, John W
Pilkington, Rhiannon M
Schuch, Helena S
Gialamas, Angela
Sawyer, Michael G
Chittleborough, Catherine R
Schurer, Stefanie
Gurrin, Lyle C
author_sort Moreno-Betancur, Margarita
collection PubMed
description BACKGROUND: 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 5 years (n = 4160) and academic achievement at 9 years (n = 6370). 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 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.
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spelling pubmed-99080502023-02-09 Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data Moreno-Betancur, Margarita Lynch, John W Pilkington, Rhiannon M Schuch, Helena S Gialamas, Angela Sawyer, Michael G Chittleborough, Catherine R Schurer, Stefanie Gurrin, Lyle C Int J Epidemiol More on RCTs BACKGROUND: 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 5 years (n = 4160) and academic achievement at 9 years (n = 6370). 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 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. Oxford University Press 2022-05-18 /pmc/articles/PMC9908050/ /pubmed/35588223 http://dx.doi.org/10.1093/ije/dyac092 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association. https://creativecommons.org/licenses/by-nc/4.0/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
spellingShingle More on RCTs
Moreno-Betancur, Margarita
Lynch, John W
Pilkington, Rhiannon M
Schuch, Helena S
Gialamas, Angela
Sawyer, Michael G
Chittleborough, Catherine R
Schurer, Stefanie
Gurrin, Lyle C
Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title_full Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title_fullStr Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title_full_unstemmed Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title_short Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
title_sort emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data
topic More on RCTs
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908050/
https://www.ncbi.nlm.nih.gov/pubmed/35588223
http://dx.doi.org/10.1093/ije/dyac092
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