Are current standards of reporting quality for clinical trials sufficient in addressing important sources of bias?

Contemporary Clinical Trials

Volumn 45, Issue , 2015, Pages 2-7

  Mills, Edward J ^a,b   Ayers, Dieter ^a   Chou, Roger ^c   Thorlund, Kristian ^a,b  

^a Redwood Outcomes   (Canada)

^b MCMASTER UNIVERSITY   (Canada)

^c OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Bias; Quality; Randomized clinical trials

Indexed keywords

ARTICLE; DOUBLE BLIND PROCEDURE; PUBLICATION; PUBLISHING; RANDOM ERROR; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAMPLE SIZE; SENSITIVITY ANALYSIS; SIMULATION; SINGLE BLIND PROCEDURE; STATISTICAL BIAS; SYSTEMATIC REVIEW (TOPIC); COMPUTER SIMULATION; HUMAN; MEASUREMENT ACCURACY; METHODOLOGY; PROCEDURES; STANDARDS;

BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; DATA ACCURACY; HUMANS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN;

EID: 84955188051     PISSN: 15517144     EISSN: 15592030     Source Type: Journal    
DOI: 10.1016/j.cct.2015.07.019     Document Type: Article

References (50)

1. D. Chavalarias, J.P. Ioannidis, Science mapping analysis characterizes 235 biases in biomedical research, J. Clin. Epidemiol. 63 (2010) 1205-1215.
2. N.D. Berkman, P.L. Santaguida, M. Viswanathan, S.C. Morton, The Empirical Evidence of Bias in Trials Measuring Treatment Differences, Agency for Healthcare Research and Quality, (Rockville (MD)), 2014.
3. K.F. Schulz, I. Chalmers, R.J. Hayes, D.G. Altman, Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials, JAMA 273 (1995) 408-412.
4. A.R. Jadad, R.A. Moore, D. Carroll, C. Jenkinson, D.J. Reynolds, D.J. Gavaghan, H.J. McQuay, Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control. Clin. Trials 17 (1996) 1-12.
5. S.A. Olivo, L.G. Macedo, I.C. Gadotti, J. Fuentes, T. Stanton, D.J. Magee, Scales to assess the quality of randomized controlled trials: a systematic review, Phys. Ther. 88 (2008) 156-175.
6. J.P. Ioannidis, J.D. Horbar, C.M. Ovelman, Y. Brosseau, K. Thorlund, M.E. Buus-Frank, E.J. Mills, R.F. Soll, Completeness of main outcomes across randomized trials in entire discipline: survey of chronic lung disease outcomes in preterm infants, BMJ 350 (2015) h72.
7. J. Savovic, H. Jones, D. Altman, R. Harris, P. Juni, J. Pildal, B. Als-Nielsen, E. Balk, C. Gluud, L. Gluud, J. Ioannidis, K. Schulz, R. Beynon, N. Welton, L. Wood, D. Moher, J. Deeks, J. Sterne, Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of metaepidemiological studies, Health Technol. Assess. 16 (2012) 1-82.
8. E.M. Balk, P.A. Bonis, H. Moskowitz, C.H. Schmid, J.P. Ioannidis, C. Wang, J. Lau, Correlation of quality measures with estimates of treatment effect in meta-analyses of randomized controlled trials, JAMA 287 (2002) 2973-2982.
9. M. Egger, P. Juni, C. Bartlett, F. Holenstein, J. Sterne, How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study, Health Technol. Assess. 7 (2003) 1-76.
10. S. Hempel, J. Miles, M. Suttorp, Detection of Associations Between Trial Quality and Effect Sizes. Methods Research Report. Prepared by the Southern California Evidence-based Practice Center Under Contract No. 290-2007-10062-I AHRQ Publication No. 12-EHC010-EF, Agency for Healthcare Research and Quality, Rockville, MD, January 2012.
11. P. Herbison, J. Hay-Smith, W.J. Gillespie, Adjustment ofmeta-analyses on the basis of quality scores should be abandoned, J. Clin. Epidemiol. 59 (2006) 1249-1256.
12. P. Herbison, J. Hay-Smith, W.J. Gillespie, Differentmethods of allocation to groups in randomized trials are associated with different levels of bias. A metaepidemiological study, J. Clin. Epidemiol. 64 (2011) 1070-1075.
13. P. Juni, A. Witschi, R. Bloch, M. Egger, The hazards of scoring the quality of clinical trials for meta-analysis, JAMA 282 (1999) 1054-1060.
14. L.L. Kjaergard, J. Villumsen, C. Gluud, Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses, Ann. Intern. Med. 135 (2001) 982-989.
15. K. Linde, M. Scholz, G. Ramirez, N. Clausius, D. Melchart, W.B. Jonas, Impact of study quality on outcome in placebo-controlled trials of homeopathy, J. Clin. Epidemiol. 52 (1999) 631-636.
16. D. Moher, D.J. Cook, A.R. Jadad, P. Tugwell, M. Moher, A. Jones, B. Pham, T.P. Klassen, Assessing the quality of reports of randomised trials: implications for the conduct of meta-analyses, Health Technol. Assess. 3 (i-iv) (1999) 1-98.
17. D. Moher, B. Pham, A. Jones, D.J. Cook, A.R. Jadad, M. Moher, P. Tugwell, T.P. Klassen, Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet 352 (1998) 609-613.
18. J. Pildal, A. Hrobjartsson, K.J. Jorgensen, J. Hilden, D.G. Altman, P.C. Gotzsche, Impact of allocation concealment on conclusions drawn from meta-analyses of randomized trials, Int. J. Epidemiol. 36 (2007) 847-857.
19. J. Savovic, H.E. Jones, D.G. Altman, R.J. Harris, P. Juni, J. Pildal, B. Als-Nielsen, E.M. Balk, C. Gluud, L.L. Gluud, J.P. Ioannidis, K.F. Schulz, R. Beynon, N.J. Welton, L. Wood, D. Moher, J.J. Deeks, J.A. Sterne, Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials, Ann. Intern. Med. 157 (2012) 429-438.
20. V. Siersma, B. Als-Nielsen, W. Chen, J. Hilden, L.L. Gluud, C. Gluud, Multivariable modelling for meta-epidemiological assessment of the association between trial quality and treatment effects estimated in randomized clinical trials, Stat. Med. 26 (2007) 2745-2758.
21. J.A. Sterne, P. Juni, K.F. Schulz, D.G. Altman, C. Bartlett, M. Egger, Statistical methods for assessing the influence of study characteristics on treatment effects in 'meta-epidemiological' research, Stat. Med. 21 (2002) 1513-1524.
22. L. Wood, M. Egger, L.L. Gluud, K.F. Schulz, P. Juni, D.G. Altman, C. Gluud, R.M. Martin, A.J. Wood, J.A. Sterne, Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study, BMJ 336 (2008) 601-605.
23. J. Zhang, K.F. Yu, What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA 280 (1998) 1690-1691.
24. L. Hartling, M. Ospina, Y. Liang, D.M. Dryden, N. Hooton, J. Krebs Seida, T.P. Klassen, Risk of bias versus quality assessment of randomised controlled trials: cross sectional study, BMJ 339 (2009) b4012.
25. M. Crowe, L. Sheppard, A review of critical appraisal tools show they lack rigor: alternative tool structure is proposed, J. Clin. Epidemiol. 64 (2011) 79-89.
26. S. Greenland, K. O'Rourke, On the bias produced by quality scores in meta-analysis, and a hierarchical view of proposed solutions, Biostatistics 2 (2001) 463-471.
27. P.J. Devereaux, P.T. Choi, S. El-Dika, M. Bhandari, V.M. Montori, H.J. Schunemann, A.X. Garg, J.W. Busse, D. Heels-Ansdell, W.A. Ghali, B.J. Manns, G.H. Guyatt, An observational study found that authors of randomized controlled trials frequently use concealment of randomization and blinding, despite the failure to report these methods, J. Clin. Epidemiol. 57 (2004) 1232-1236.
28. E. Nuesch, S. Trelle, S. Reichenbach, A.W. Rutjes, B. Tschannen, D.G. Altman, M. Egger, P. Juni, Small study effects in meta-analyses of osteoarthritis trials: metaepidemiological study, BMJ 341 (2010) c3515.
29. S.G. Moreno, A.J. Sutton, A.E. Ades, N.J. Cooper, K.R. Abrams, Adjusting for publication biases across similar interventions performed well when compared with gold standard data, J. Clin. Epidemiol. 64 (2011) 1230-1241.
30. S. Parekh-Bhurke, C.S. Kwok, C. Pang, L. Hooper, Y.K. Loke, J.J. Ryder, A.J. Sutton, C.B. Hing, I. Harvey, F. Song, Uptake of methods to deal with publication bias in systematic reviews has increased over time, but there is still much scope for improvement, J. Clin. Epidemiol. 64 (2011) 349-357.
31. E. Wager, P. Williams, "Hardlyworth the effort"? Medical journals' policies and their editors' and publishers' views on trial registration and publication bias: quantitative and qualitative study, BMJ 347 (2013) f5248.
32. N.S. Young, J.P. Ioannidis, O. Al-Ubaydli, Why current publication practices may distort science, PLoS Med. 5 (2008) e201.
33. M.E. Flacco, L. Manzoli, S. Boccia, L. Capasso, K. Aleksovska, A. Rosso, G. Scaioli, C. De Vito, R. Siliquini, P. Villari, J.P. Ioannidis, Head-to-head randomized trials are mostly industry sponsored and almost always favor the industry sponsor, J. Clin. Epidemiol. 68 (7) (2015) 811-820.
34. D.G. Contopoulos-Ioannidis, E. Ntzani, J.P. Ioannidis, Translation of highly promising basic science research into clinical applications, Am. J. Med. 114 (2003) 477-484.
35. J.P. Ioannidis, Effectiveness of antidepressants: an evidencemyth constructed froma thousand randomized trials? Philos. Ethics Humanit. Med. 3 (2008) 14.
36. J.P. Ioannidis, F.B. Karassa, E. Druyts, K. Thorlund, E.J. Mills, Biologic agents in rheumatology: unmet issues after 200 trials and $200 billion sales, Nat. Rev. Rheumatol. 9 (2013) 665-673.
37. F.L. Schmidt, I.S. Oh, T.L. Hayes, Fixed-versus random-effects models in metaanalysis: model properties and an empirical comparison of differences in results, Br. J. Math. Stat. Psychol. 62 (2009) 97-128.
38. E.J. Mills, S. Kanters, K. Thorlund, A. Chaimani, A.A. Veroniki, J.P. Ioannidis, The effects of excluding treatments from network meta-analyses: survey, BMJ 347 (2013) f5195.
39. A.D. Oxman, J.N. Lavis, A. Fretheim, Use of evidence inWHO recommendations, Lancet 369 (2007) 1883-1889.
40. C.S. Wiysonge, G.D. Hussey, Use of systematic reviews in WHO recommendations, Lancet 377 (2011) 2006.
41. S. Dias, N.J. Welton, V.C.C. Marinho, G. Salanti, J.P.T. Higgins, A.E. Ades, Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta-analysis, J. R. Stat. Soc. A. Stat. Soc. 173 (2010) 613-629.
42. R.M. Turner, D.J. Spiegelhalter, G.C. Smith, S.G. Thompson, Bias modelling in evidence synthesis, J. R. Stat. Soc. Ser. A 172 (2009) 21-47.
43. D.C. Hoaglin, N. Hawkins, J.P. Jansen, D.A. Scott, R. Itzler, J.C. Cappelleri, C. Boersma, D. Thompson, K.M. Larholt, M. Diaz, A. Barrett, Conducting indirect-treatmentcomparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2, Value Health 14 (2011) 429-437.
44. J.P. Jansen, R. Fleurence, B. Devine, R. Itzler, A. Barrett, N. Hawkins, K. Lee, C. Boersma, L. Annemans, J.C. Cappelleri, Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1, Value Health 14 (2011) 417-428.
45. E.J. Mills, S. Kelly, M. Bradley, P. Mollon, C. Cooper, J. Nachega, Antiretroviral effects on HIV-1 RNA, CD4 cell count and progression to AIDS or death: a meta-regression analysis, HIV Med. 9 (2008) 849-857.
46. J.P. Higgins, D.G. Altman, P.C. Gotzsche, P. Juni, D. Moher, A.D. Oxman, J. Savovic, K.F. Schulz, L. Weeks, J.A. Sterne, Cochrane Bias Methods G, Cochrane Statistical Methods G. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials, BMJ 343 (2011) d5928.
47. S. Yusuf, R. Collins, R. Peto, Why do we need some large, simple randomized trials? Stat. Med. 3 (1984) 409-422.
48. P.M. Rothwell, External validity of randomised controlled trials: "to whomdo the results of this trial apply?", Lancet 365 (2005) 82-93.
49. S.C. Howard, A. Algra, C.P. Warlow, P.M. Rothwell, Potential consequences for recruitment, power, and external validity of requirements for additional risk factors for eligibility in randomized controlled trials in secondary prevention of stroke, Stroke 37 (2006) 209-215.
50. J. Higgins, D.G. Altman, Assessing risk of bias in included studies, in: J.P.T. Higgins, S. Green (Eds.), Cochrane Handbook for Systematic Reviews of Interventions, Wiley 2008, pp. 187-241.