Detecting outlying trials in network meta-analysis

Statistics in Medicine

Volumn 34, Issue 19, 2015, Pages 2695-2707

  Zhang, Jing ^a   Fu, Haoda ^b   Carlin, Bradley P ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b ELI LILLY AND COMPANY   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Detection measures; Network meta analysis; Trial level outliers

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; CALCULATION; DATA ANALYSIS; DIABETES MELLITUS; NETWORK META ANALYSIS; NORMAL DISTRIBUTION; PREDICTION; PROBABILITY; SIMULATION; STANDARDIZATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STUDY DESIGN; CLINICAL TRIAL (TOPIC); HUMAN; META ANALYSIS (TOPIC); PROCEDURES; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

ANTIDIABETIC AGENT;

BAYES THEOREM; BIAS (EPIDEMIOLOGY); CLINICAL TRIALS AS TOPIC; HUMANS; HYPOGLYCEMIC AGENTS; META-ANALYSIS AS TOPIC; NORMAL DISTRIBUTION;

EID: 84935585285     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6509     Document Type: Article

References (36)

1. Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Statistics in Medicine 2004; 23(20):3105-3124.
2. Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. JASA 2006; 101(474):447-459.
3. Lu G, Ades AE, Sutton AJ, Cooper NJ, Briggs AH, Caldwell DM. Meta-analysis of mixed treatment comparisons at multiple follow-up times. Statistics in Medicine 2007; 26(20):3681-3699.
4. Lu G, Ades AE. Modeling between-trial variance structure in mixed treatment comparisons. Biostatistics 2009; 10(4):792-805.
5. Hong H, Chu H, Zhang J, Carlin BP. A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons. Research-Report 2012-018, Division of Biostatisitcs, University of Minnesota, Submitted to Research Synthesis Methods, 2014.
6. Zhang J, Carlin BP, Neaton JD, Soon GG, Nie L, Kane R, Virnig BA, Chu H. Network meta-analysis of randomized clinical trials: reporting the proper summaries. Clinical Trials 2014; 11(2):246-262.
7. Zhang J, Chu H, Hong H, Neaton JD, Virnig BA, Carlin BP, Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness. Research Report 2013-018, Division of Biostatistics, University of Minnesota.
8. Senn S, Gavini F, Magrez D, Scheen A. Issues in performing a network meta-analysis. Statistical Methods in Medical Research 2013; 22(2):169-189.
9. Lu G, Welton NJ, Higgins J, White IR, Ades AE. Linear inference for mixed treatment comparison meta-analysis: a two-stage approach. Research Synthesis Methods 2011; 2(1):43-60.
10. Piepho HP, Williams ER, Madden LV. The use of two-way linear mixed models in multitreatment meta-analysis. Biometrics 2012; 68(4):1269-1277.
11. Jackson D, Barrett JK, Rice S, White IR, Higgins JPT. A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Statistics in Medicine 2014; 33(21):3639-3654.
12. White IR, Barrett JK, Jackson D, Higgins J. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 2012; 3(2):111-125.
13. Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods 2012; 3(2):98-110.
14. Hedges LV, Olkin L. Statistical Methods for Meta-Analysis. Academic Press: New York, 1985.
15. Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods 2010; 1(2):112-125.
16. Welton NJ, Sutton AJ, Cooper NJ, Abrams KR, Ades AE. Evidence Synthesis for Decision Making in Healthcare. John Wiley and Sons: West Sussex, 2012.
17. Carlin BP, Louis TA. Bayesian Methods for Data Analysis 3rd edn. Chapman and Hall/CRC: Boca Raton, 2009.
18. Rücker G, Schwarzer G, Krahn U. Package 'netmeta': network meta-analysis with R. R package version 0.4-2, 2014.
19. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley & Sons: Chichester, 2004.
20. Ding Y, Fu H. Bayesian indirect and mixed treatment comparisons across longitudinal time points. Statistics in Medicine 2012; 32(15):2613-2628.
21. Chu H, Nie L, Chen Y, Huang Y, Sun W. Bivariate random effects models for meta-analysis of comparative studies with binary outcomes: methods for the absolute risk difference and relative risk. Statistical Methods in Medical Research 2012; 21(6):621-633.
22. Rubin DB. Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics 1984; 12(4):1151-1172.
23. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis 3rd edn. Chapman and Hall/CRC: Boca Raton, 2013.
24. Gelman A, Meng XL, Stern HS. Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica 1996; 6(4):733-807.
25. Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association 1993; 88(422):669-679.
26. Madan J, Stevenson MD, Cooper KL, Ades AE, Whyte S, Akehurst R. Consistency between direct and indirect trial evidence: is direct evidence always more reliable?Value in Health 2011; 14(6):953-960.
27. Marshall EC, Spiegelhalter DJ. Approximate cross-validatory predictive checks in disease mapping models. Statistics in Medicine 2003; 22(10):1649-1660.
28. Ohlssen D, Price KL, Xia H, Hong H, Kerman J, Fu H, Quartey G, Heilmean C, Ma H, Carlin BP. Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis. Pharmaceutical Statistics 2014; 13:55-70.
29. van Tulder MW, Assendelft WJJ, Koes BW, Bouter LM. Method guidelines for systematic reviews in the Cochrane collaboration back review group for spinal disorders. Spine 1997; 22(20):2323-2330.
30. Turner RM, Spiegelhalter DJ, Smith G, Thompson SG. Bias modelling in evidence synthesis. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009; 172(1):21-47.
31. Welton NJ, Ades AE, Carlin JB, Altman DG, Sterne JAC. Models for potentially biased evidence in meta-analysis using empirically based priors. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009; 172(1):119-136.
32. Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine 2010; 29(7-8):932-944.
33. Ibrahim JG, Chen MH. Power prior distributions for regression models. Statistical Science 2000; 15(1):46-60.
34. Hobbs BP, Carlin BP, Mandrekar SJ, Sargent DJ. Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics 2011; 67(3):1047-1056.
35. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. John Wiley and Sons: West Sussex, 2011.
36. Aitken AC. On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh 1936; 55:42-48.