Meta-analysis of studies with missing data

Biometrics

Volumn 65, Issue 2, 2009, Pages 487-496

  Yuan, Ying ^a   Little, Roderick J A ^b  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Bias; Nonignorable missing data; Patient level missing data; Random effects model

Indexed keywords

PATIENT TREATMENT;

BAYESIAN; BIAS; EFFECT SIZE; META-ANALYSIS; MISSING DATA; NONIGNORABLE MISSING DATA; PATIENT-LEVEL MISSING DATA; RANDOM EFFECTS MODEL; TREATMENT EFFECTS;

RANDOM PROCESSES;

BIOMETRY; ESTIMATION METHOD; HETEROGENEITY; META-ANALYSIS; SIZE EFFECT;

ANALYSIS OF VARIANCE; ARTICLE; ASSAY; BAYES THEOREM; DATA ANALYSIS; EFFECT SIZE; MATHEMATICAL MODEL; MEDICAL RESEARCH; METHODOLOGY; PSYCHOLOGIC TEST; RANDOMIZATION; SIMULATION;

EID: 66949121681     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01068.x     Document Type: Article

References (44)

1. Albert, J. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88, 669 679.
2. Albert, P. S. and Follmann, D. (2000). Modeling repeated count data subject to informative dropout. Biometrics 56, 667 677.
3. Altman, D. G. (2000). Statistics in medical journals: Some recent trends. Statistics in Medicine 19, 3275 3289.
4. Arends, L. R., Hoes, A. W., Lubsen, J., Grobbee, D. E., and Stijnen, T. (2000). Baseline risk as predictor of treatment benefit: Three clinical meta-re-analyses. Statistics in Medicine 19, 3497 3518. (Pubitemid 32056699)
5. Bailey, K. R. (1987). Inter-study differences - how should they influence the interpretation and analysis of results. Statistics in Medicine 6, 351 360.
6. Baker, R. and Jackson, D. (2006). Using journal impact factors to correct for the publication bias of medical studies. Biometrics 62, 785 792.
7. Biggerstaff, B. J. and Tweedie, R. L. (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine 16, 753 768. (Pubitemid 27165681)
8. Brand, R. and Kragt, H. (1992). Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials. Statistics in Medicine 11, 2077 2082. (Pubitemid 23066840)
9. Bushman, B. J. and Wang, M. C. (1995). A procedure for combining sample correlation coefficients and vote counts to obtain an estimate and confidence interval for the population correlation coefficient. Psychological Bulletin 117, 530 546.
10. Bushman, B. J. and Wang, M. C. (1996). A procedure for combining sample standardized mean differences and vote counts to estimate the population standardized mean difference in fixed effect models. Psychological Method 1, 66 80. (Pubitemid 126421254)
11. Cochran, W. G. (1937). Problem arising in the analysis of a series of similar experiments. Journal of the Royal Statistical Society Supplement 4 (1 102 118.
12. Copas, J. (1999). What works?: Selectivity models and meta-analysis. Journal of the Royal Statistical Society, Series A 162, 95 109.
13. Dear, K. B. G. and Begg, C. B. (1992). An approach for assessing publication bias prior to performing a meta-analysis. Statistical Science 7, 237 245.
14. De Gruttola, V. and Tu, X. M. (1994). Modelling progression of CD4 lymphocyte count and its relationship to survival time. Biometrics 50, 1003 1014.
15. DerSimonian, R and Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7, 177 188. (Pubitemid 17189972)
16. Duval, S. and Tweedie, R. (2000). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association 95, 89 98.
17. Follmann, D. and Wu, M. C. (1995). An approximate generalized linear model with random effects for informative missing data. Biometrics 51, 151 168.
18. Fleiss, J. L. (1993). The statistical basis of meta-analysis. Statistical Methods in Medical Research 2, 121 145.
19. Gelfand, A. E., Hills, S. E., Racine-Poon, A., and Smith, A. F. M. (1990). Illustration of Bayesian inference in normal data models using Gibbs' sampling. Journal of the American Statistical Association 85, 972 985.
20. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515 533.
21. Gelman, A., Meng, X., and Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica 6, 733 807. (Pubitemid 126706389)
22. Hardy, R. J. and Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine 15, 619 629. (Pubitemid 26109009)
23. Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Education Statistics 6, 107 128.
24. Hedges, L. V. (1992). Modeling publication selection effects in meta-analysis. Statistical Science 7, 237 245.
25. Henmi, M., Copas, J., and Eguchi, S. (2007). Confidence intervals and p-values for meta-analysis with publication bias. Biometrics 63, 475 482.
26. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edition. New York : John Wiley & Sons.
27. Louis, T. A. and Zelterman, D. (1994). Bayesian approaches to research synthesis. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (eds 411 422. New York : Russell Sage Foundation.
28. McIntosh, M. W. (1996). The population risk as an explanatory variable in research syntheses of clinical trials. Statistics in Medicine 15, 1713 1728. (Pubitemid 26280302)
29. Mosteller, F. and Colditz, G. A. (1996). Understanding research synthesis (meta-analysis). Annual Review of Public Health 61, 714 719.
30. Pampallona, S., Bollini, P., Tibaldi, G., Kupelnick, B., and Munizza, C. (2004). Combined pharmacotherapy and psychological treatment for depression. Archives of General Psychiatry 61, 714 719.
31. Pigott, T. D. (1994). Methods for handling missing data in research synthesis. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (eds 163 176. New York : Russell Sage Foundation.
32. Rosenthal, R. (1978). Combining the results to independent studies. Professional Psychology 17, 136 137.
33. Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin 86, 638 641.
34. Rothstein, H., Sutton, A. J., and Borenstein, M. (2005). Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. New York : John Wiley & Sons.
35. Rotnitzky, A., Robins, J. M., and Scharfstein, D. O. (1998). Semiparametric regression for repeated outcomes with non-ignorable non-response. Journal of the American Statistical Association 93, 1321 1339. (Pubitemid 128385545)
36. Rotnitzky, A., Scharfstein, D., Su, T. L., and Robins, J. (2001). Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring. Biometrics 57, 103 113. (Pubitemid 32200143)
37. Rubin, D. B. (1976). Inference and missing data (with discussion). Biometrika 63, 581 592.
38. Shadish, W. R. and Haddock, C. K. (1994). Combining estimates of effect size. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (eds 261 281. New York : Russell Sage Foundation.
39. Sterling, T. D. (1959). Publication decisions and their possible effects on inferences drawn test of significance - or vice versa. Journal of American Statistical Association 54, 30 34.
40. Sutton, A. J., Abrams, K. R., Jones, D. R., Sheldon, T. A., and Song, F. (2000). Methods for Meta-Analysis in Medical Research. New York : John Wiley & Sons.
41. Ten Have, T. R., Pulkstenis, E., Kunselman, A., and Landis, J. R. (1998). Mixed effects logistic regression models for longitudinal binary response data with informative dropout. Biometrics 54, 367 383. (Pubitemid 28158811)
42. Walter, S. D. (1997). Variation in baseline risk as an explanation of heterogeneity in meta-analysis. Statistics in Medicine 16, 2883 2900. (Pubitemid 27520639)
43. Wu, M. C. and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 44, 175 188. (Pubitemid 18098821)
44. Yuan, Y. and Little, R. J. A. (2007). Model-based estimates of the finite population mean for two-stage cluster samples with unit nonresponse. Applied Statistics 56, 79 97.