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Count outcome meta-analysis for comparing treatments by fusing mixed data sources

Count outcome meta-analysis for comparing treatments by fusing mixed data sources
Count outcome meta-analysis for comparing treatments by fusing mixed data sources
Assessing interventions applied to target populations is a matter of prime interest. Studies are usually undertaken to see whether an alternative intervention is superior (or at least equivalent) to a comparable standard intervention. This is typically achieved by comparing alternative and standard intervention within a given study, and the developed meta-analytic methodology is building on this assumption. Very little work has been delivered when studies only report results on one of the interventions only, but not on both. This is the situation we consider here, and it is motivated by study reports on two surgeries for treatment of asymptomatic antenatally diagnosed congenital lung malformations in young children. Reports are often only available for one of the two, and restricting analysis on those with results on both surgeries will restrict data to 33% of the potential sources. We show in this paper how data sources can be fused and under which condition this fusion will provide valid results. Application to the case study shows the potential gain of the suggested approach in reaching a more conclusive analysis. We argue that studies should best allow within-study comparison, but if only one intervention information is available (for example, as the required surgery expertise for the comparative intervention is not deliverable at the respective site), harnessing one-group information can provide additional insights.
1863-8171
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Sangnawakij, Patarawan
e821a2a7-a89f-4172-9006-8a6c2db9add6
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Sangnawakij, Patarawan
e821a2a7-a89f-4172-9006-8a6c2db9add6

Bohning, Dankmar and Sangnawakij, Patarawan (2020) Count outcome meta-analysis for comparing treatments by fusing mixed data sources. AStA Advances in Statistical Analysis. (doi:10.1007/s10182-020-00370-9).

Record type: Article

Abstract

Assessing interventions applied to target populations is a matter of prime interest. Studies are usually undertaken to see whether an alternative intervention is superior (or at least equivalent) to a comparable standard intervention. This is typically achieved by comparing alternative and standard intervention within a given study, and the developed meta-analytic methodology is building on this assumption. Very little work has been delivered when studies only report results on one of the interventions only, but not on both. This is the situation we consider here, and it is motivated by study reports on two surgeries for treatment of asymptomatic antenatally diagnosed congenital lung malformations in young children. Reports are often only available for one of the two, and restricting analysis on those with results on both surgeries will restrict data to 33% of the potential sources. We show in this paper how data sources can be fused and under which condition this fusion will provide valid results. Application to the case study shows the potential gain of the suggested approach in reaching a more conclusive analysis. We argue that studies should best allow within-study comparison, but if only one intervention information is available (for example, as the required surgery expertise for the comparative intervention is not deliverable at the respective site), harnessing one-group information can provide additional insights.

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More information

Accepted/In Press date: 14 May 2020
e-pub ahead of print date: 11 June 2020
Published date: 2020

Identifiers

Local EPrints ID: 443800
URI: http://eprints.soton.ac.uk/id/eprint/443800
ISSN: 1863-8171
PURE UUID: c33b77b8-9644-4332-bc66-12bf45898a94
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 14 Sep 2020 16:30
Last modified: 26 Nov 2021 02:57

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Contributors

Author: Dankmar Bohning ORCID iD
Author: Patarawan Sangnawakij

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