Allowing for uncertainty due to missing data in meta-analysis - Part 1: Two-stage methods

Statistics in Medicine

Volumn 27, Issue 5, 2008, Pages 711-727

  White, Ian R ^a   Higgins, Julian P T ^a   Wood, Angela M ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Bayesian methods; Informative priors; Meta analysis; Missing data; Sensitivity analysis

Indexed keywords

PSYCHOTROPIC AGENT;

ARTICLE; AUTOMUTILATION; BAYES THEOREM; BIVARIATE ANALYSIS; CLINICAL TRIAL; CONFIDENCE INTERVAL; HUMAN; INTERMETHOD COMPARISON; MEDICAL RESEARCH; METHODOLOGY; OUTCOME ASSESSMENT; PSYCHOSOCIAL CARE; QUANTITATIVE ANALYSIS; SENSITIVITY ANALYSIS; STATISTICAL MODEL; SYSTEMATIC ERROR; TREATMENT FAILURE; TREATMENT OUTCOME; UNCERTAINTY; VARIANCE;

BAYES THEOREM; BIAS (EPIDEMIOLOGY); META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME; UNCERTAINTY;

EID: 39549121035     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3008     Document Type: Article

References (31)

1. Little RJA, Rubin DB. Statistical Analysis with Missing data (2nd edn). Wiley: Hoboken, NJ, 2002.
2. White IR, Moodie E, Thompson SG, Croudace T. A modelling strategy for the analysis of clinical trials with partly missing longitudinal data. International Journal of Methods in Psychiatric Research 2003; 12:139-150.
3. Brown CH. Protecting against nonrandomly missing data in longitudinal studies. Biometrics 1990; 46:143-155.
4. Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics 2000; 1:465-480.
5. Touloumi G, Babiker AG, Pocock SJ, Darbyshire JH. Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study. Statistics in Medicine 2001; 20:3715-3728.
6. Rubin DB. Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association 1977; 72:538-543.
7. Forster JJ, Smith PWF. Model-based inference for categorical survey data subject to non-ignorable non-response. Journal of the Royal Statistical Society (B) 1998; 60:57-70.
8. Raghunathan TE. Nonparametric analysis of randomized experiments with missing covariate and outcome data - comment. Journal of the American Statistical Association 2000; 95:85-87.
9. Scharfstein DO, Daniels MJ, Robins JM. Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. Biostatistics 2003; 4:495-512.
10. Greenland S. Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society (A) 2005; 168:267-306.
11. White IR, Carpenter J, Evans S, Schroter S. Eliciting and using expert opinions about non-response bias in randomised controlled trials. Clinical Trials 2007; 4:125-139.
12. Higgins JPT, White IR, Wood A. Missing outcome data in meta-analysis of clinical trials: development and comparison of methods, with recommendations for practice. Clinical Trials, submitted.
13. Gamble C, Hollis S. Uncertainty method improved on best-worst case analysis in a binary meta-analysis. Journal of Clinical Epidemiology 2005; 58:579-588.
14. 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, in this issue. DOI: 10.1002/ sim.3007.
15. Hawton K, Townsend E, Arensman E, Gunnell D, Hazell P, House A, van Heeringen K. Psychosocial and pharmacological treatments for deliberate self harm. The Cochrane Library, Issue 3. Update Software: Oxford, 2003.
16. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. British Medical Journal 2003; 327:557-560.
17. Little RJA. Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 1995; 90:1112-1121.
18. Little RJA. Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 1993; 88:125-134.
19. Gustafson P. On model expansion, model contraction, identifiability and prior information: two illustrative scenarios involving mismeasured variables. Statistical Science 2005; 20:111-140.
20. Kenward MG. Selection models for repeated measurements with non-random dropout: an illustration of sensitivity. Statistics in Medicine 1998; 17:2723-2732.
21. Magder LS. Simple approaches to assess the possible impact of missing outcome information on estimates of risk ratios, odds ratios, and risk differences. Controlled Clinical Trials 2003; 24:411-421.
22. Carpenter J, Pocock S, Lamm CJ. Coping with missing data in clinical trials: a model based approach applied to asthma trials. Statistics in Medicine 2002; 21:1043-1066.
23. Garthwaite PH, Kadane JB, O'Hagan A. Statistical methods for eliciting probability distributions. Journal of the American Statistical Association 2005; 100:680-701.
24. DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials 1986; 7:177-188.
25. Hollis S. A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome. Statistics in Medicine 2002; 21:3823-3834.
26. Kadane JB, Wolfson LJ. Experiences in elicitation. Journal of the Royal Statistical Society (D) 1998; 47:3-19.
27. O'Hagan A. Eliciting prior beliefs in substantial practical applications. Journal of the Royal Statistical Society (D) 1998; 47:21-25.
28. Parmar MK, Spiegelhalter DJ, Freedman LS. The CHART trials - Bayesian design and monitoring in practice. Statistics in Medicine 1994; 13:1297-1312.
29. White IR, Pocock SJ, Wang D. Eliciting and using expert opinions about influence of patient characteristics on treatment effects: a Bayesian analysis of the CHARM trials. Statistics in Medicine 2005; 24:3805-3821.
30. Wood AM, White IR, Hotopf M. Using number of failed contact attempts to adjust for non-ignorable non-response. Journal of the Royal Statistical Society (A) 2006; 169:525-542.
31. Sweeting MJ, Sutton AJ, Lambert PC. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Statistics in Medicine 2004; 23:1351-1375.