Between-trial heterogeneity in meta-analyses may be partially explained by reported design characteristics

Journal of Clinical Epidemiology

Volumn 95, Issue , 2018, Pages 45-54

  Rhodes, Kirsty M ^a   Turner, Rebecca M ^a,b   Savović, Jelena ^c,d   Jones, Hayley E ^c   Mawdsley, David ^e   Higgins, Julian P T ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF BRISTOL   (United Kingdom)

^d UNIVERSITY HOSPITALS BRISTOL NHS FOUNDATION TRUST   (United Kingdom)

^e UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Allocation concealment; Blinding; Heterogeneity; Meta analysis; Randomized trials; Sequence generation

Indexed keywords

ALL CAUSE MORTALITY; ARTICLE; BAYES THEOREM; HUMAN; META ANALYSIS; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; RISK; STATISTICAL BIAS; STUDY DESIGN; SYSTEMATIC REVIEW; UNIVARIATE ANALYSIS; VARIANCE; EPIDEMIOLOGY; META ANALYSIS (TOPIC); METHODOLOGY; STATISTICAL ANALYSIS;

BAYES THEOREM; BIAS; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC RESEARCH DESIGN; HUMANS; META-ANALYSIS AS TOPIC; RESEARCH DESIGN;

EID: 85042481693     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2017.11.025     Document Type: Article

References (24)

1. Turner, R.M., Davey, J., Clarke, M.J., Thompson, S.G., Higgins, J.P., Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol 41 (2012), 818–827.
2. Rhodes, K.M., Turner, R.M., Higgins, J.P.T., Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol 68 (2015), 52–60.
3. Higgins, J.P.T., Thompson, S.G., Spiegelhalter, D.J., A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc 172 (2009), 137–159.
4. Schulz, K.F., Chalmers, I., Hayes, R.J., Altman, D.G., Empirical evidence of bias: Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 273 (1995), 408–412.
5. Savović, J., Turner, R.M., Mawdsley, D., Jones, H.E., Beynon, R., Higgins, J.P.T., et al. Association Between Risk-of-Bias Assessments and Results of Randomized Trials in Cochrane Reviews: the ROBES Meta-Epidemiologic Study. Am J Epidemiol, 2017 http://dx.doi.org/10.1093/aje/kwx344. [Epub ahead of print].
6. Savović, J., Jones, H., Altman, D., Harris, R., Jűni, P., Pildal, J., et al. Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of meta-epidemiological studies. Health Technol Assess 16 (2012), 1–82.
7. Higgins, J.P.T., Altman, D.G., Gøtzsche, P.C., Jüni, P., Moher, D., Oxman, A.D., et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 343, 2011, d5928.
8. Welton, N.J., Ades, A.E., Carlin, J.B., Altman, D.G., Sterne, J.A.C., Models for potentially biased evidence in meta-analysis using empirically based priors. J R Stat Soc Ser A Stat Soc 172:1 (2009), 119–136.
9. Rhodes, K.M., Mawdsley, D., Turner, R.M., Jones, H.E., Savović, J., Higgins, J.P.T., Label-invariant models for the analysis of meta-epidemiological data. Stat Med 37:1 (2018), 60–70.
10. Higgins, J.P.T., Altman, D.G., Sterne, J.A.C., Chapter 8: assessing risk of bias in included studies. Higgins, J.P.T., Green, S., (eds.) Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0, 2011 (updated March 2011) The Cochrane Collaboration, 2011. Available at www.handbook.cochrane.org.
11. Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:4 (2000), 325–337.
12. Brooks, S.P., Gelman, A., General methods for monitoring convergence of iterative simulations. J Comput Graphical Stat 7 (1998), 434–455.
13. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van Der Linde, A., Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:4 (2002), 583–639.
14. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Linde, A., The deviance information criterion: 12 years on. J R Stat Soc Ser B Stat Methodol 76:3 (2014), 485–493.
15. Welton, N.J., Ades, A.E., A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence. J R Stat Soc Ser C Appl Stat 54:2 (2005), 385–404.
16. Page, M.J., Higgins, J.P.T., Clayton, G., Sterne, J.A.C., Hróbjartsson, A., Savović, J., Empirical evidence of study design biases in randomized trials: systematic review of meta-epidemiological studies. PLoS One, 11, 2016, e0159267.
17. Riley, R.D., Higgins, J.P.T., Deeks, J.J., Interpretation of random effects meta-analyses. BMJ, 342, 2011, d549.
18. Ades, A.E., Lu, G., Higgins, J.P.T., The interpretation of random-effects meta-analysis in decision models. Med Decis Making 25 (2005), 646–654.
19. Turner, R.M., Spiegelhalter, D.J., Smith, G.C.S., Thompson, S.G., Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc 172:1 (2009), 21–47.
20. Huwiler-Müntener, K., Jüni, P., Junker, C., Egger, M., Quality of reporting of randomized trials as a measure of methodologic quality. JAMA 287 (2002), 2801–2804.
21. Hill, C.L., LaValley, M.P., Felson, T., Discrepancy between published report and actual conduct of randomized clinical trials. J Clin Epidemiol 55 (2002), 783–786.
22. Hrobjartsson, A., Pildal, J., Chan, A., Haahr, M., Altman, D., Gotzsche, P., Reporting on blinding in trial protocols and corresponding publications was often inadequate but rarely contradictory. J Clin Epidemiol 62 (2009), 967–973.
23. Dwan, K., Altman, D.G., Arnaiz, J.A., Bloom, J., Chan, A.W., Cronin, E., et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS One, 3, 2008, e3081.
24. Kirkham, J.J., Dwan, K.M., Altman, D.G., Gamble, C., Dodd, S., Smyth, R., et al. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. BMJ, 340, 2010, c365.