Meta-analysis of summary survival curve data
The use of standard univariate fixed‐ and random‐effects models in meta‐analysis has become well known in the last 20 years. However, these models are unsuitable for meta‐analysis of clinical trials that present multiple survival estimates (usually illustrated by a survival curve) during a follow‐up...
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| Veröffentlicht in: | Statistics in medicine 2008-09, Vol.27 (22), p.4381-4396 |
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| container_title | Statistics in medicine |
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| creator | Arends, Lidia R. Hunink, M. G. Myriam Stijnen, Theo |
| description | The use of standard univariate fixed‐ and random‐effects models in meta‐analysis has become well known in the last 20 years. However, these models are unsuitable for meta‐analysis of clinical trials that present multiple survival estimates (usually illustrated by a survival curve) during a follow‐up period. Therefore, special methods are needed to combine the survival curve data from different trials in a meta‐analysis. For this purpose, only fixed‐effects models have been suggested in the literature. In this paper, we propose a multivariate random‐effects model for joint analysis of survival proportions reported at multiple time points and in different studies, to be combined in a meta‐analysis. The model could be seen as a generalization of the fixed‐effects model of Dear (Biometrics 1994; 50:989–1002). We illustrate the method by using a simulated data example as well as using a clinical data example of meta‐analysis with aggregated survival curve data. All analyses can be carried out with standard general linear MIXED model software. Copyright © 2008 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.3311 |
| format | Article |
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We illustrate the method by using a simulated data example as well as using a clinical data example of meta‐analysis with aggregated survival curve data. All analyses can be carried out with standard general linear MIXED model software. 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G. Myriam</creatorcontrib><creatorcontrib>Stijnen, Theo</creatorcontrib><title>Meta-analysis of summary survival curve data</title><title>Statistics in medicine</title><addtitle>Statist. Med</addtitle><description>The use of standard univariate fixed‐ and random‐effects models in meta‐analysis has become well known in the last 20 years. However, these models are unsuitable for meta‐analysis of clinical trials that present multiple survival estimates (usually illustrated by a survival curve) during a follow‐up period. Therefore, special methods are needed to combine the survival curve data from different trials in a meta‐analysis. For this purpose, only fixed‐effects models have been suggested in the literature. In this paper, we propose a multivariate random‐effects model for joint analysis of survival proportions reported at multiple time points and in different studies, to be combined in a meta‐analysis. The model could be seen as a generalization of the fixed‐effects model of Dear (Biometrics 1994; 50:989–1002). We illustrate the method by using a simulated data example as well as using a clinical data example of meta‐analysis with aggregated survival curve data. All analyses can be carried out with standard general linear MIXED model software. 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The model could be seen as a generalization of the fixed‐effects model of Dear (Biometrics 1994; 50:989–1002). We illustrate the method by using a simulated data example as well as using a clinical data example of meta‐analysis with aggregated survival curve data. All analyses can be carried out with standard general linear MIXED model software. Copyright © 2008 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>18465839</pmid><doi>10.1002/sim.3311</doi><tpages>16</tpages></addata></record> |
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| ispartof | Statistics in medicine, 2008-09, Vol.27 (22), p.4381-4396 |
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| language | eng |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Analysis of Variance Bone Marrow Transplantation Clinical Trials as Topic - methods Computer Simulation Disease-Free Survival Humans Kaplan-Meier Estimate meta-analysis Meta-Analysis as Topic Models, Statistical multivariate random effects model Neoplasms - drug therapy Neoplasms - surgery Probability time to event |
| title | Meta-analysis of summary survival curve data |
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