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Sequential methods for random-effects meta-analysis

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Sequential methods for random-effects meta-analysis. / Higgins, Julian P. T.; Whitehead, Anne; Simmonds, Mark.
In: Statistics in Medicine, Vol. 30, No. 9, 30.04.2011, p. 903-921.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Higgins, JPT, Whitehead, A & Simmonds, M 2011, 'Sequential methods for random-effects meta-analysis', Statistics in Medicine, vol. 30, no. 9, pp. 903-921. https://doi.org/10.1002/sim.4088

APA

Higgins, J. P. T., Whitehead, A., & Simmonds, M. (2011). Sequential methods for random-effects meta-analysis. Statistics in Medicine, 30(9), 903-921. https://doi.org/10.1002/sim.4088

Vancouver

Higgins JPT, Whitehead A, Simmonds M. Sequential methods for random-effects meta-analysis. Statistics in Medicine. 2011 Apr 30;30(9):903-921. Epub 2010 Dec 28. doi: 10.1002/sim.4088

Author

Higgins, Julian P. T. ; Whitehead, Anne ; Simmonds, Mark. / Sequential methods for random-effects meta-analysis. In: Statistics in Medicine. 2011 ; Vol. 30, No. 9. pp. 903-921.

Bibtex

@article{cabadd9ba98d4ddc84dbf1686cfc990f,
title = "Sequential methods for random-effects meta-analysis",
abstract = "Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers.",
keywords = "meta-analysis, sequential methods, cumulative meta-analysis , prospective meta-analysis , prior distributions",
author = "Higgins, {Julian P. T.} and Anne Whitehead and Mark Simmonds",
year = "2011",
month = apr,
day = "30",
doi = "10.1002/sim.4088",
language = "English",
volume = "30",
pages = "903--921",
journal = "Statistics in Medicine",
issn = "1097-0258",
publisher = "John Wiley and Sons Ltd",
number = "9",

}

RIS

TY - JOUR

T1 - Sequential methods for random-effects meta-analysis

AU - Higgins, Julian P. T.

AU - Whitehead, Anne

AU - Simmonds, Mark

PY - 2011/4/30

Y1 - 2011/4/30

N2 - Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers.

AB - Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers.

KW - meta-analysis

KW - sequential methods

KW - cumulative meta-analysis

KW - prospective meta-analysis

KW - prior distributions

U2 - 10.1002/sim.4088

DO - 10.1002/sim.4088

M3 - Journal article

VL - 30

SP - 903

EP - 921

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 9

ER -