The Resource Correcting for publication bias in the presence of covariates, prepared for, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by, Minnesota Evidence-based Practice Center ; investigators, Sue Duval, Eric Weinhandl, (electronic resource)

Correcting for publication bias in the presence of covariates, prepared for, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by, Minnesota Evidence-based Practice Center ; investigators, Sue Duval, Eric Weinhandl, (electronic resource)

Label
Correcting for publication bias in the presence of covariates
Title
Correcting for publication bias in the presence of covariates
Statement of responsibility
prepared for, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by, Minnesota Evidence-based Practice Center ; investigators, Sue Duval, Eric Weinhandl
Creator
Contributor
Subject
Genre
Language
  • eng
  • eng
Summary
OBJECTIVES: To date, there are no established methods for assessing publication bias when study characteristics induce heterogeneity in the effects. The "trim and fill" method was developed to adjust for censored (i.e., missing) studies in a meta-analysis, assumed due to publication bias. We sought to modify this algorithm for use in the context where study characteristics induce heterogeneity in the effects. METHODS: An iterative algorithm based on the original trim and fill algorithm was developed. We performed Monte Carlo simulations with 5,000 iterations per instance of the adapted trim and fill algorithm. In each instance we set six parameters, both to alter the structure of the randomly generated data, and to manipulate the algorithm itself. We assessed the average performance (type 1 error, power, bias) of the algorithm, in the context of inference regarding the metaregression parameters. We also applied the method to data from 19 randomized studies examining the hypothesis that teachers' expectations influence students' IQ intelligence test scores, the covariate of interest being the dichotomized length of teacher-student contact prior to the study. We developed user-friendly software in R, for one covariate at this stage, with future versions to incorporate several covariates. RESULTS: Meaningful, albeit incomplete, reduction in the bias of estimated metaregression model parameters was achieved. Bias and coverage probability improved as the number of studies increased. The R estimator outperformed both L and Q from the original trim and fill method. Performance declined in the presence of large heterogeneity, but substantial bias reduction was still obtained. Two algorithm variants were developed, with the simpler one-dimensional version performing slightly better than the two-dimensional. CONCLUSIONS: This new method provides a generalized trim and fill algorithm that is applicable to metaregression, that is, where covariates are available. The new algorithm should be seen as a sensitivity analysis to the influence of covariates on funnel plot asymmetry
Member of
Cataloging source
DNLM
http://library.link/vocab/creatorName
Duval, Sue
Funding information
Prepared for: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, 540 Gaither Road, Rockville, MD 20850; www.ahrq.gov Contract No. 290-02-0009. Prepared by: Minnesota Evidence-based Practice Center, Minneapolis, MN
Government publication
federal national government publication
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
NLM call number
WA 950
http://library.link/vocab/relatedWorkOrContributorName
  • Weinhandl, Eric
  • United States
  • Minnesota Evidence-based Practice Center
Series statement
  • Methods research report
  • AHRQ publication
Series volume
no. 11-EHC041-EF
http://library.link/vocab/subjectName
  • Meta-Analysis as Topic
  • Publication Bias
  • Data Interpretation, Statistical
  • Algorithms
Label
Correcting for publication bias in the presence of covariates, prepared for, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by, Minnesota Evidence-based Practice Center ; investigators, Sue Duval, Eric Weinhandl, (electronic resource)
Instantiates
Publication
Note
"September 2011."
Bibliography note
Includes bibliographical references
Dimensions
unknown
Extent
1 online resource (PDF file)
Form of item
online
Other physical details
ill.
Specific material designation
remote
System control number
  • 1581855
  • (DNLM)BKSHLF:NBK84231
Label
Correcting for publication bias in the presence of covariates, prepared for, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by, Minnesota Evidence-based Practice Center ; investigators, Sue Duval, Eric Weinhandl, (electronic resource)
Publication
Note
"September 2011."
Bibliography note
Includes bibliographical references
Dimensions
unknown
Extent
1 online resource (PDF file)
Form of item
online
Other physical details
ill.
Specific material designation
remote
System control number
  • 1581855
  • (DNLM)BKSHLF:NBK84231

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