Application of the matched nested case-control design to the secondary analysis of trial data.

Feeding Matching Neonatology Nested case-control Preterm infants Randomised controlled trial Statistical methods

Journal

BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545

Informations de publication

Date de publication:
14 05 2020
Historique:
received: 03 12 2019
accepted: 05 05 2020
entrez: 16 5 2020
pubmed: 16 5 2020
medline: 25 6 2021
Statut: epublish

Résumé

A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data. We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants. Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and necrotising enterocolitis. A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial.

Sections du résumé

BACKGROUND
A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data.
METHODS
We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants.
RESULTS
Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and necrotising enterocolitis.
CONCLUSIONS
A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial.

Identifiants

pubmed: 32410578
doi: 10.1186/s12874-020-01007-w
pii: 10.1186/s12874-020-01007-w
pmc: PMC7227268
doi:

Types de publication

Journal Article Randomized Controlled Trial Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

117

Subventions

Organisme : Action Medical Research
ID : GN2506
Pays : International

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Auteurs

Christopher Partlett (C)

Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, UK. chris.partlett@nottingham.ac.uk.
National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. chris.partlett@nottingham.ac.uk.

Nigel J Hall (NJ)

University Surgery Unit, Faculty of Medicine, University of Southampton, Southampton, UK.

Alison Leaf (A)

National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Department of Child Health, Faculty of Medicine, University of Southampton, Southampton, UK.

Edmund Juszczak (E)

National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Louise Linsell (L)

National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

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Classifications MeSH