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Correcting for Publication Bias in the Presence of Covariates
Methods Research Reports
Investigators: Sue Duval, PhD and Eric Weinhandl, MS.
Author Information and AffiliationsStructured Abstract
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.
Contents
Prepared for: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services1, Contract No. 290-02-0009. Prepared by: Minnesota Evidence-based Practice Center, Minneapolis, MN
Suggested citation:
Duval S, Weinhandl E. Correcting for Publication Bias in the Presence of Covariates. Methods Research Report. (Prepared by the Minnesota Evidence-based Practice Center under Contract No. 290-02-0009.) AHRQ Publication No. 11-EHC041-EF. Rockville, MD: Agency for Healthcare Research and Quality. September 2011. Available at: www.effectivehealthcare.ahrq.gov/reports/final.cfm.
This report is based on research conducted by the Minnesota Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 290-02-0009, Task Order Number: 1, Work Assignment Number 1). The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
The information in this report is intended to help clinicians, employers, policymakers, and others make informed decisions about the provision of health care services. This report is intended as a reference and not as a substitute for clinical judgment.
This report may be used, in whole or in part, as the basis for the development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.
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.ahrq.gov
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