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Book: Individual participant data meta-analysis

Riley, Richard D. / Tierney, Jayne F. / Stewart, Lesley A.

a handbook for healthcare research

(Statistics in practice)

2021

Author's details	edited by Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart
Series title	Statistics in practice
Keywords	Gesundheitswesen ; Metaanalyse ; Statistisches Modell ; Randomisierung
Subject	Randomisation ; Statistik ; Meta-Analyse ; Gesundheitsdienst ; Gesundheitssystem ; Gesundheitswirtschaft ; Medizinalwesen ; Medizinalsystem
Language	English
Size	ix, 550 Seiten, Illustrationen
Publisher	Wiley
Publishing place	Hoboken, NJ
Publishing country	United States
Document type	Book
HBZ-ID	HT020312639
ISBN	978-1-119-33372-2 ; 9781119333760 ; 9781119333784 ; 1-119-33372-5 ; 1119333768 ; 1119333784
Database	Catalogue ZB MED Medicine, Health

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Shelf mark	Loan status	Location
2021 A 764	available for loan (reading room)	Lesesaal EG

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Article ; Online: Sharing individual participant data: through a systematic reviewer lens.

Rydzewska, Larysa H M / Stewart, Lesley A / Tierney, Jayne F

Trials

2022 Volume 23, Issue 1, Page(s) 167

Abstract: An increasing prevalence of data-sharing models, aimed at making individual participant data (IPD) from clinical trials widely available, should facilitate the conduct of systematic reviews and meta-analyses based on IPD. We have assessed these different ...

Abstract	An increasing prevalence of data-sharing models, aimed at making individual participant data (IPD) from clinical trials widely available, should facilitate the conduct of systematic reviews and meta-analyses based on IPD. We have assessed these different data-sharing approaches, from the perspective of experienced IPD reviewers, to examine their utility for conducting systematic reviews based on IPD, and to highlight any challenges. We present an overview of the range of different models, including the traditional, single question approach, topic-based repositories, and the newer generic data platforms, and show that there are benefits and drawbacks to each. In particular, not all of the new models allow researchers to fully realise the well-documented advantages of using IPD for meta-analysis, and we offer potential solutions that can help improve both data quantity and utility. However, to achieve the "nirvana" of an ideal clinical data sharing environment, both for IPD meta-analysis and other secondary research purposes, we propose that data providers, data requestors, funders, and platforms need to adopt a more joined-up and standardised approach.
MeSH term(s)	Data Analysis ; Humans ; Information Dissemination ; Research Personnel ; Systematic Reviews as Topic
Language	English
Publishing date	2022-02-21
Publishing country	England
Document type	Letter ; Meta-Analysis ; Review
ZDB-ID	2040523-6
ISSN	1745-6215 ; 1468-6694 ; 1745-6215
ISSN (online)	1745-6215
ISSN	1468-6694 ; 1745-6215
DOI	10.1186/s13063-021-05787-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Meta-analyses based on summary data can provide timely, thorough and reliable evidence: don't dismiss them yet.

Godolphin, Peter J / Rogozińska, Ewelina / Fisher, David J / Vale, Claire L / Tierney, Jayne F

Nature medicine

2022 Volume 28, Issue 3, Page(s) 429–430

MeSH term(s)	Ivermectin
Chemical Substances	Ivermectin (70288-86-7)
Language	English
Publishing date	2022-02-10
Publishing country	United States
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-021-01675-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 4212: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Sharing individual participant data

Larysa H. M. Rydzewska / Lesley A. Stewart / Jayne F. Tierney

Trials, Vol 23, Iss 1, Pp 1-

through a systematic reviewer lens

2022 Volume 9

Abstract: Abstract An increasing prevalence of data-sharing models, aimed at making individual participant data (IPD) from clinical trials widely available, should facilitate the conduct of systematic reviews and meta-analyses based on IPD. We have assessed these ... ...

Abstract	Abstract An increasing prevalence of data-sharing models, aimed at making individual participant data (IPD) from clinical trials widely available, should facilitate the conduct of systematic reviews and meta-analyses based on IPD. We have assessed these different data-sharing approaches, from the perspective of experienced IPD reviewers, to examine their utility for conducting systematic reviews based on IPD, and to highlight any challenges. We present an overview of the range of different models, including the traditional, single question approach, topic-based repositories, and the newer generic data platforms, and show that there are benefits and drawbacks to each. In particular, not all of the new models allow researchers to fully realise the well-documented advantages of using IPD for meta-analysis, and we offer potential solutions that can help improve both data quantity and utility. However, to achieve the “nirvana” of an ideal clinical data sharing environment, both for IPD meta-analysis and other secondary research purposes, we propose that data providers, data requestors, funders, and platforms need to adopt a more joined-up and standardised approach.
Keywords	Data sharing ; Systematic review ; Clinical trials ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework.

Godolphin, Peter J / White, Ian R / Tierney, Jayne F / Fisher, David J

Research synthesis methods

2022 Volume 14, Issue 1, Page(s) 68–78

Abstract: Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need ... ...

Abstract	Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.
MeSH term(s)	Humans ; Research Design ; Bias
Language	English
Publishing date	2022-07-28
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2548499-0
ISSN	1759-2887 ; 1759-2879
ISSN (online)	1759-2887
ISSN	1759-2879
DOI	10.1002/jrsm.1590
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses.

Godolphin, Peter J / Marlin, Nadine / Cornett, Chantelle / Fisher, David J / Tierney, Jayne F / White, Ian R / Rogozińska, Ewelina

Research synthesis methods

2023 Volume 15, Issue 1, Page(s) 107–116

Abstract: Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored ... ...

Abstract	Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice.
MeSH term(s)	Humans ; Models, Statistical ; Meta-Analysis as Topic ; Randomized Controlled Trials as Topic
Language	English
Publishing date	2023-09-28
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2548499-0
ISSN	1759-2887 ; 1759-2879
ISSN (online)	1759-2887
ISSN	1759-2879
DOI	10.1002/jrsm.1674
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Duplicated network meta-analysis in advanced prostate cancer: a case study and recommendations for change.

Fisher, David J / Burdett, Sarah / Vale, Claire / White, Ian R / Tierney, Jayne F

Systematic reviews

2022 Volume 11, Issue 1, Page(s) 274

Abstract: Background: Research overlap and duplication is a recognised problem in the context of both pairwise and network systematic reviews and meta-analyses. As a case study, we carried out a scoping review to identify and examine duplicated network meta- ... ...

Abstract	Background: Research overlap and duplication is a recognised problem in the context of both pairwise and network systematic reviews and meta-analyses. As a case study, we carried out a scoping review to identify and examine duplicated network meta-analyses (NMAs) in a specific disease setting where several novel therapies have recently emerged: hormone-sensitive metastatic prostate cancer (mHSPC). Methods: MEDLINE and EMBASE were systematically searched, in January 2020, for indirect or mixed treatment comparisons or network meta-analyses of the systemic treatments docetaxel and abiraterone acetate in the mHSPC setting, with a time-to-event outcome reported on the hazard-ratio scale. Eligibility decisions were made, and data extraction performed, by two independent reviewers. Results: A total of 13 eligible reviews were identified, analysing between 3 and 8 randomised comparisons, and comprising between 1773 and 7844 individual patients. Although the included trials and treatments showed a high degree of overlap, we observed considerable variation between identified reviews in terms of review aims, eligibility criteria and included data, statistical methodology, reporting and inference. Furthermore, crucial methodological details and specific source data were often unclear. Conclusions and recommendations: Variation across duplicated NMAs, together with reporting inadequacies, may compromise identification of best-performing treatments. Particularly in fast-moving fields, review authors should be aware of all relevant studies, and of other reviews with potential for overlap or duplication. We recommend that review protocols be published in advance, with greater clarity regarding the specific aims or scope of the project, and that reports include information on how the work builds upon existing knowledge. Source data and results should be clearly and completely presented to allow unbiased interpretation.
MeSH term(s)	Male ; Humans ; Network Meta-Analysis ; Abiraterone Acetate ; Prostatic Neoplasms/drug therapy ; Docetaxel/therapeutic use
Chemical Substances	Abiraterone Acetate (EM5OCB9YJ6) ; Docetaxel (15H5577CQD)
Language	English
Publishing date	2022-12-16
Publishing country	England
Document type	Systematic Review ; Meta-Analysis ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2662257-9
ISSN	2046-4053 ; 2046-4053
ISSN (online)	2046-4053
ISSN	2046-4053
DOI	10.1186/s13643-022-02137-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Implications of analysing time-to-event outcomes as binary in meta-analysis: empirical evidence from the Cochrane Database of Systematic Reviews.

Salika, Theodosia / Turner, Rebecca M / Fisher, David / Tierney, Jayne F / White, Ian R

BMC medical research methodology

2022 Volume 22, Issue 1, Page(s) 73

Abstract: Background: Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on ... ...

Abstract	Background: Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures. Methods: We extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only ("binary"), or in binary form together with observed minus expected and variance statistics ("OEV"). We explored how results for time-to-event outcomes originally analysed as "binary" change when analysed using the complementary log-log (clog-log) link on a HR scale. For the data originally analysed as HRs ("OEV"), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale. Results: The pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses. Conclusions: We identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log-log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses.
MeSH term(s)	Humans ; Meta-Analysis as Topic ; Odds Ratio ; Proportional Hazards Models ; Research Design ; Systematic Reviews as Topic
Language	English
Publishing date	2022-03-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-022-01541-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Implications of analysing time-to-event outcomes as binary in meta-analysis

Theodosia Salika / Rebecca M. Turner / David Fisher / Jayne F. Tierney / Ian R. White

BMC Medical Research Methodology, Vol 22, Iss 1, Pp 1-

empirical evidence from the Cochrane Database of Systematic Reviews

2022 Volume 14

Abstract: Abstract Background Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be ... ...

Abstract	Abstract Background Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures. Methods We extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only (“binary”), or in binary form together with observed minus expected and variance statistics (“OEV”). We explored how results for time-to-event outcomes originally analysed as “binary” change when analysed using the complementary log–log (clog-log) link on a HR scale. For the data originally analysed as HRs (“OEV”), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale. Results The pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses. Conclusions We identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log–log link may be a useful alternative when analysing ...
Keywords	Time-to-event ; Meta-analysis ; Methodology ; Survival data ; Clinical trials ; Cochrane database of systematic reviews ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using individual participant data to improve network meta-analysis projects.

Riley, Richard D / Dias, Sofia / Donegan, Sarah / Tierney, Jayne F / Stewart, Lesley A / Efthimiou, Orestis / Phillippo, David M

BMJ evidence-based medicine

2022 Volume 28, Issue 3, Page(s) 197–203

Abstract: A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent ...

Abstract	A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.
MeSH term(s)	Humans ; Network Meta-Analysis ; Meta-Analysis as Topic
Language	English
Publishing date	2022-08-10
Publishing country	England
Document type	Journal Article
ISSN	2515-4478
ISSN (online)	2515-4478
DOI	10.1136/bmjebm-2022-111931
Database	MEDical Literature Analysis and Retrieval System OnLINE

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