A parsimonious weight function for modeling publication bias

Psychological Methods

Volumn 22, Issue 1, 2017, Pages 28-41

  Citkowicz, Martyna ^a   Vevea, Jack L ^b,c  

^a NORTHWESTERN UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c AMERICAN INSTITUTES FOR RESEARCH   (United States)

Author keywords

Meta analysis; Publication bias; Selection; Weight function model

Indexed keywords

ANALYTIC METHOD; EFFECT SIZE; HUMAN; META ANALYSIS; PUBLISHING; SCIENTIST; SIMULATION; META ANALYSIS (TOPIC); PSYCHOLOGY;

META-ANALYSIS AS TOPIC; PSYCHOLOGY; PUBLICATION BIAS;

EID: 85014401671     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/met0000119     Document Type: Article

References (55)

1. Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. Annals of Family Medicine, 10, 63-74.
2. Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50, 1088-1101.
3. Biggerstaff, B. J., & Tweedie, R. L. (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine, 16, 753-768.
4. Copas, J. B. (1999). What works?: Selectivity models and meta-analysis. Journal of the Royal Statistical Society, 162, 95-109.
5. Copas, J. B., & Li, H. G. (1997). Inference for non-random samples. Journal of the Royal Statistical Society, 59, 55-95.
6. Copas, J. B., & Shi, J. Q. (2001). A sensitivity analysis for publication bias in systematic reviews. Statistical Methods in Medical Research, 10, 251-265. http://dx.doi.org/10.1177/096228020101000402
7. Coursol, A., & Wagner, E. E. (1986). Effect of positive findings on submission and acceptance rates: A note on meta-analysis bias. Professional Psychology: Research and Practice, 17, 136-137.
8. Dear, K. B. G., & Begg, C. B. (1992). An approach for assessing publication bias prior to performing a meta-analysis. Statistical Science, 7, 237-245.
9. DeGroot, M. H., & Schervish, M. J. (2002). Probability and statistics (3rd ed.). Boston, MA: Addison Wesley.
10. Dickersin, K. (2005). Publication bias: Recognizing the problem, understanding its origins and scope, and preventing harm. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis: Prevention, assessments, and adjustments (pp. 11-33). West Sussex, UK: Wiley and Sons, Ltd.
11. Duval, S., & Tweedie, R. (2000a). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95, 89-98.
12. Duval, S., & Tweedie, R. (2000b). Trim and fill: A simple funnel-plotbased method of testing and adjusting for publication bias in metaanalysis. Biometrics, 56, 455-463.
13. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315, 629-634.
14. Ferguson, C. J., & Brannick, M. T. (2012). Publication bias in psychological science: Prevalence, methods for identifying and controlling, and implications for the use of meta-analyses. Psychological Methods, 17, 120-128.
15. Gilbody, S. M., Song, F., Eastwood, A. J., & Sutton, A. (2000). The causes, consequences and detection of publication bias in psychiatry. Acta Psychiatrica Scandinavica, 102, 241-249.
16. Greenwald, A. G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82, 1-20.
17. Hedges, L. V. (1984). Estimation of effect size under nonrandom sampling: The effects of censoring studies yielding statistically insignificant mean differences. Journal of Education Statistics, 9, 61-85.
18. Hedges, L. V. (1992a). Meta-analysis. Journal of Education Statistics, 17, 279-296.
19. Hedges, L. V. (1992b). Modeling publication selection effects in metaanalysis. Statistical Science, 7, 246-255.
20. Hedges, L. V., & Pigott, T. D. (2001). The power of statistical tests in meta-analysis. Psychological Methods, 6, 203-217.
21. Hedges, L. V., & Vevea, J. L. (1998). Fixed-and random-effects models in meta-analysis. Psychological Methods, 3, 486-504.
22. Hedges, L. V., & Vevea, J. (2005). Selection method approaches. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis: Prevention, assessments, and adjustments (pp. 145-174). West Sussex, UK: Wiley and Sons, Ltd.
23. Iyengar, S., & Greenhouse, J. B. (1988). Selection models and the file drawer problem. Statistical Science, 3, 109-135.
24. Johnson, N. L., & Kotz, S. (1970). Continuous univariate distributions: Distributions in statistics. Wiley series in probability and mathematical statistics. New York, NY: Wiley and Son, Inc.
25. Light, R. J., & Pillemer, D. B. (1984). Summing up: The science of reviewing research. Cambridge, MA: Harvard University Press.
26. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.
27. Macaskill, P., Walter, S. D., & Irwig, L. (2001). A comparison of methods to detect publication bias in meta-analysis. Statistics in Medicine, 20, 641-654.
28. Mooney, C. Z. (1997). Monte Carlo simulation. Sage series in quantitative applications in the social sciences (Vol. 116). Thousand Oaks, CA: Sage.
29. Moreno, S. G., Sutton, A. J., Thompson, J. R., Ades, A. E., Abrams, K. R., & Cooper, N. J. (2012). A generalized weighting regression-derived meta-analysis estimator robust to small-study effects and heterogeneity. Statistics in Medicine, 31, 1407-1417.
30. National Research Council. (1992). Combining information: Statistical issues and research opportunities. Washington, DC: National Academy Press.
31. Nelson, N., Rosenthal, R., & Rosnow, R. L. (1986). Interpretation of significance levels and effect sizes by psychological researchers. American Psychologist, 41, 1299-1301.
32. Patil, G. P., Rao, C. R., & Zelen, M. (1988). Weighted distributions. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 9, pp. 565-571). New York, NY: John Wiley & Sons, Inc.
33. Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2006). Comparison of two methods to detect publication bias in metaanalysis. Journal of the American Medical Association, 295, 676-680.
34. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical recipes in Fortran 77: The art of scientific computing (2nd ed.). Cambridge, MA: New York, NY: Cambridge University Press.
35. R Core Team. (2013). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org/
36. Rice, J. A. (2007). Mathematical statistics and data analysis (3rd ed.). Belmont, CA: Brooks/Cole, Cengage Learning.
37. Rosenthal, R. (1979). The "file drawer problem" and tolerance for null results. Psychological Bulletin, 86, 638-641.
38. Rosenthal, R., & Gaito, J. (1963). The interpretation of levels of significance by psychological researchers. Journal of Psychology, 55, 33-38.
39. Rosenthal, R., & Gaito, J. (1964). Further evidence for the cliff effect in the interpretation of levels of significance. Psychological Reports, 15, 570.
40. Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2005). Publication bias in meta-analysis. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis: Prevention, assessments, and adjustments (pp. 1-7). West Sussex, UK: Wiley and Sons, Ltd.
41. Rücker, G., Schwarzer, G., & Carpenter, J. (2008). Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine, 27, 746-763.
42. Rücker, G., Schwarzer, G., Carpenter, J. R., Binder, H., & Schumacher, M. (2011). Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics, 12, 122-142.
43. Schwarzer, G., Antes, G., & Schumacher, M. (2007). A test for publication bias in meta-analysis with sparse binary data. Statistical Medicine, 26, 721-733.
44. Stanley, T. D. (2008). Meta-regression methods for detecting and estimating empirical effects in the presence of publication selection. Oxford Bulletin of Economics and Statistics, 70, 103-127.
45. Sterling, T. D. (1959). Publication decisions and their possible effects on inferences drawn from tests of significance-or vice versa. Journal of the American Statistical Association, 54, 30-34.
46. Terrin, N., Schmid, C. H., Lau, J., & Olkin, I. (2003). Adjusting for publication bias in the presence of heterogeneity. Statistics in Medicine, 22, 2113-2126.
47. The Campbell Collaboration. (2010). About us. Retrieved from http:// www.campbellcollaboration.org/about_us/index.php
48. The Cochrane Collaboration. (2010). About us. Retrieved from http://www .cochrane.org/about-us
49. Thisted, R. A. (1988). Elements of statistical computing. New York, NY: Chapman and Hall.
50. Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18, 2693-2708.
51. Veitch, E. (2006). Introducing PLoS clinical trials: A different approach to publishing trial results. PLoS Clinical Trials, 1, 1.
52. Vevea, J. L., & Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60, 419-435.
53. Vevea, J. L., & Hedges, L. V. (1996, April). Meta-analysis under publication bias: Compensating for selection in small data sets. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
54. Vevea, J. L., & Woods, C. M. (2005). Publication bias in research synthesis: Sensitivity analysis using a priori weight functions. Psychological Methods, 10, 428-443.
55. Weinhandl, E. D., & Duval, S. (2012). Generalization of trim and fill for application in meta-regression. Research Synthesis Methods, 3, 51-67.