Meta-regression with partial information on summary trial or patient characteristics

Statistics in Medicine

Volumn 29, Issue 12, 2010, Pages 1312-1324

  Hemming, K ^a   Hutton, J L ^b   Maguire, M J ^c   Marson, A G ^c  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

Bayesian analysis; Meta regression; Missing covariates

Indexed keywords

VIGABATRIN;

AGE DISTRIBUTION; ANALYSIS OF COVARIANCE; ANTICONVULSANT THERAPY; ARTICLE; BAYES THEOREM; CASE STUDY; DATA ANALYSIS; DOSE CALCULATION; EPILEPSY; FACTORIAL ANALYSIS; HUMAN; META ANALYSIS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; STATISTICAL MODEL; TREATMENT DURATION; VISUAL FIELD DEFECT;

ANTICONVULSANTS; BAYES THEOREM; BIOSTATISTICS; CLINICAL TRIALS AS TOPIC; HUMANS; LIKELIHOOD FUNCTIONS; META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; VIGABATRIN;

EID: 77952835714     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3848     Document Type: Article

References (27)

1. Sutton AJ, Higgins JP. Recent developments in meta analysis. Statistics in Medicine 2008; 27:625-650.
2. MJ MJM, Hemming K, Hutton JL, Marson AG. Overwhelming heterogeneity in systematic reviews of observational anti-epileptic studies. Epilepsy Research 2008; 80:210-212.
3. Higgins JPT, Thomson SG. How should meta-regression be undertaken and interpreted? Statistics in Medicine 2002; 21:1559-1573.
4. Smith TC, Spiegelhalter DJ, Thomas A. Bayesian approaches to random-effects meta-analysis: a comparative study. Statistics in Medicine 1995; 14:2685-2699. (Pubitemid 26014258)
5. Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Methods for Meta-analysis in Medical Research. Statistics in Practice. Wiley: London, 2000.
6. Little RJA, Rubin DB. Statistical Analysis with Missing Data (2nd edn). Wiley series in probability and statistics. Wiley: New York, 2002.
7. White IR, Higgins JPT, Wood AM. Allowing for uncertainty due to missing data in meta-analysis - Part 1: two stage methods. Statistics in Medicine 2008; 27:711-727.
8. White IR, Welton NJ, Wood AM, Ades AE, Higgins JPT. Allowing for uncertainty due to missing data in meta-analysis - Part 2: hierarchical models. Statistics in Medicine 2008; 27:728-745.
9. Spiegelhalter DJ, Thomas A, Best NG. WinBUGS Version 1.2 User Manual, 1999. Available from: http://www.mrc-bsu.cam.ac.uk/bugs/.
10. Ibrahim JG, Lipsitz SR, Chen MH. Missing covariates in generalized linear models when the missing data mechanism is non-ignorable. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 1999; 61:173-190.
11. Christian PR, Casella G. Monte Carlo Statistical Methods (2nd edn). Springer: New York, 2004.
12. Brooks SP, Gelman A. Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 1998; 7:434-455. (Pubitemid 128635035)
13. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2002; 64:583-639.
14. Carlin BP, Lewis TA. Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall: London, 1996.
15. Gelfand AE, Sahu SK, Carlin BP. Efficient parameterisations for normal linear mixed models. Biometrika 1995; 82:479-488.
16. Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Statistics in Medicine 2005; 24:2401-2428.
17. McInturff P, Johnson WO, Cowling D, Gardner IA. Modelling risk when binary outcomes are subject to error. Statistics in Medicine 2004; 23:1095-1109.
18. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006; 1:515-533.
19. Burns T, Catty J, Dash M, Roberts C, Lockwood A, Marshall M. Use of intensive case management to reduce time in hospital in people with severe mental illness: a systematic review and meta-regression. BMJ 2007; 335:336-343.
20. Chen MH, Ibrahim JG, Lipsitz SR. Bayesian methods for missing covariates in cure rate models. Lifetime Data Analysis 2002; 8:117-146.
21. Higgins JPT, Thompson SG. Controlling the risk of spurious findings form meta-regression. Statistics in Medicine 2004; 23:1663-1682.
22. Thompson SG, Higgins JPT. Can meta-analysis help target interventions at individuals most likely to benefit? The Lancet 2005; 365:341-365.
23. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ 1996; 312:1215-1218. (Pubitemid 26136714)
24. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluations. Statistics in Practice. Wiley: London, 2004.
25. Schafer JL. Multiple Imputation: a primer. Statistical Methods in Medical Research 1999; 8:3-15.
26. Barnard J, Meng XL. Applications of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research 1999; 8:17-36.
27. Horton NJ, Laird NM. Maximum likelihood analysis of generalized linear models with missing covariates. Statistical Methods in Medical Research 1999; 8:37-50.