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Simulation-based power calculations for planning a two-stage individual participant data meta-analysis

BACKGROUND: Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for...

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Autores principales: Ensor, Joie, Burke, Danielle L., Snell, Kym I. E., Hemming, Karla, Riley, Richard D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960205/
https://www.ncbi.nlm.nih.gov/pubmed/29776399
http://dx.doi.org/10.1186/s12874-018-0492-z
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author Ensor, Joie
Burke, Danielle L.
Snell, Kym I. E.
Hemming, Karla
Riley, Richard D.
author_facet Ensor, Joie
Burke, Danielle L.
Snell, Kym I. E.
Hemming, Karla
Riley, Richard D.
author_sort Ensor, Joie
collection PubMed
description BACKGROUND: Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions. METHODS: The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value. RESULTS: In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials. CONCLUSIONS: Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0492-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-59602052018-05-24 Simulation-based power calculations for planning a two-stage individual participant data meta-analysis Ensor, Joie Burke, Danielle L. Snell, Kym I. E. Hemming, Karla Riley, Richard D. BMC Med Res Methodol Research Article BACKGROUND: Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions. METHODS: The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value. RESULTS: In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials. CONCLUSIONS: Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0492-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-18 /pmc/articles/PMC5960205/ /pubmed/29776399 http://dx.doi.org/10.1186/s12874-018-0492-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ensor, Joie
Burke, Danielle L.
Snell, Kym I. E.
Hemming, Karla
Riley, Richard D.
Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title_full Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title_fullStr Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title_full_unstemmed Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title_short Simulation-based power calculations for planning a two-stage individual participant data meta-analysis
title_sort simulation-based power calculations for planning a two-stage individual participant data meta-analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960205/
https://www.ncbi.nlm.nih.gov/pubmed/29776399
http://dx.doi.org/10.1186/s12874-018-0492-z
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