Imputation of missing variance data using non-linear mixed effects modelling to enable an inverse variance weighted meta-analysis of summary-level longitudinal data: A case study

Pharmaceutical Statistics

Volumn 11, Issue 4, 2012, Pages 318-324

  Boucher, Martin ^a  

^a PFIZER GLOBAL RESEARCH AND DEVELOPMENT   (United Kingdom)

Author keywords

meta analysis; missing variance data; non linear modelling

Indexed keywords

DICLOFENAC; NAPROXEN; OXYCODONE; PLACEBO;

ARTICLE; BAYESIAN LEARNING; BIOSIS; CONFIDENCE INTERVAL; DATA BASE; DECISION MAKING; DRUG EFFICACY; EMBASE; HIP OSTEOARTHRITIS; HUMAN; INFORMATION PROCESSING; KNEE OSTEOARTHRITIS; LONGITUDINAL STUDY; MEDLINE; META ANALYSIS (TOPIC); NON LINEAR MIXED EFFECTS MODEL; PAIN ASSESSMENT; RANDOMIZED CONTROLLED TRIAL (TOPIC); RATING SCALE; STATISTICAL MODEL; VARIANCE; WESTERN ONTARIO AND MCMASTER UNIVERSITIES PAIN SCORE;

BAYES THEOREM; CLINICAL TRIALS AS TOPIC; DATA INTERPRETATION, STATISTICAL; ENDPOINT DETERMINATION; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; NONLINEAR DYNAMICS;

EID: 84863718574     PISSN: 15391604     EISSN: 15391612     Source Type: Journal    
DOI: 10.1002/pst.1515     Document Type: Article

References (13)

1. Wiebe N, Vandermeer B, Platt RW, Klassen TP, Moher D, Barrowman NJ,. A systematic review identifies a lack of standardization in methods for handling missing variance data. Journal of Clinical Epidemiology 2006; 59: 342-353.
2. Stevens JW,. A note on dealing with missing standard errors in meta-analyses of continuous outcome measures in WinBUGS. Pharmaceutical Statistics 2011; 10: 374-378. DOI: 10.1002/pst.491.
3. Furukawa TA, Barbui C, Cipriani A, Brambilla P, Watanabe N,. Imputing missing standard deviations in meta-analyses can provide accurate results. Journal of Clinical Epidemiology 2006; 59: 7-10. (Pubitemid 41797469)
4. Cranney A, Tugwell P, Wells G, Guyatt G,. Meta-analyses of therapies for postmenopausal osteoporosis: I. Systematic reviews of randomized trials in osteoporosis: introduction and methodology. Endocrine Reviews 2002; 23: 496-507.
5. Carrigan G, Barnett AG, Dobson AJ, Mishra G,. Compensating for missing data from longitudinal studies using WinBUGS. Journal of Statistical Software 2007; 19 (7): 1-17.
6. Bellamy N, (ed). WOMAC Osteoarthritis Index: A User's Guide. The Western Ontario and McMaster Universities: Ontario, 1995.
7. Robertson C, Idris NR, Boyle P,. Beyond classical meta-analysis: can inadequately reported studies be included? Drug Discovery Today 2004; 9 (21): 924-931. (Pubitemid 39389053)
8. Gelman A, Carlin JB, Stern HS, Rubin DB,. Bayesian Data Analysis. Chapman and Hall/CRC: Boca Raton, Florida, 2004.
9. SAS/STAT User's Guide, Version 8. SAS Institute Inc: Cary, NC, 1999.
10. Lunn DJ, Thomas A, Best N, Spiegelhalter D,. WinBUGSâa Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10: 325-337.
11. Gelman A,. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006; 1 (3): 515-533.
12. Chan A, Hro'bjartsson A, Haahr MT, GÃtzsche PC, Altman DG,. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA 2004; 291: 2457-2465. (Pubitemid 38669189)
13. Neuenschwander B, Capkun-Niggli G, Branson M, Spiegelhalter DJ,. Summarizing historical information on controls in clinical trials. Clinical Trials 2010; 7: 5-18.