Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome - a simulation study

PLoS ONE

Volumn 7, Issue 5, 2012, Pages

  Chu, Rong ^a   Walter, Stephen D ^a   Guyatt, Gordon ^a   Devereaux, P J ^a,b   Walsh, Michael ^a   Thorlund, Kristian ^a   Thabane, Lehana ^a,b,c,d  

^a MCMASTER UNIVERSITY   (Canada)

^b MCMASTER UNIVERSITY   (Canada)

^c MCMASTER UNIVERSITY   (Canada)

^d CENTRE FOR EVALUATION OF MEDICINES   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLINICAL ASSESSMENT; CONTROLLED STUDY; HUMAN; MAJOR CLINICAL STUDY; PARALLEL DESIGN; PREVALENCE; PROBABILITY; PROGNOSIS; RANDOMIZED CONTROLLED TRIAL; RISK ASSESSMENT; RISK FACTOR; SIMULATION;

COMPUTER SIMULATION; HUMANS; LOGISTIC MODELS; OUTCOME ASSESSMENT (HEALTH CARE); PROBABILITY; PROGNOSIS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RISK; SAMPLE SIZE;

EID: 84861313018     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0036677     Document Type: Article

References (35)

1. Guyatt GH, Haynes RB, Jaeschke RZ, Cook DJ, Green L, et al. (2000) Users' Guides to the Medical Literature: XXV. Evidence-based medicine: principles for applying the Users' Guides to patient care. Evidence-Based Medicine Working Group. JAMA 284: 1290-1296.
2. Senn S, (1994) Testing for baseline balance in clinical trials. Stat Med 13: 1715-1726.
3. Wang SJ, O'Neill RT, Hung HJ, (2010) Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials. Clin Trials 7: 525-536.
4. Brower RG, Lanken PN, MacIntyre N, Matthay MA, Morris A, et al. (2004) Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome. N Engl J Med 351: 327-336.
5. Meade MO, Cook DJ, Guyatt GH, Slutsky AS, Arabi YM, et al. (2008) Ventilation strategy using low tidal volumes, recruitment maneuvers, and high positive end-expiratory pressure for acute lung injury and acute respiratory distress syndrome: a randomized controlled trial. JAMA 299: 637-645.
6. The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 333: 1581-1587.
7. Demchuk AM, Tanne D, Hill MD, Kasner SE, Hanson S, et al. (2001) Predictors of good outcome after intravenous tPA for acute ischemic stroke. Neurology 57: 474-480.
8. Robinson LD, Jewell NP, (1991) Some surprising results about covariate adjustment in logistic regression models. International Statistical Review 58: 227-240.
9. Lingsma H, Roozenbeek B, Steyerberg E, IMPACT investigators (2010) Covariate adjustment increases statistical power in randomized controlled trials. J Clin Epidemiol 63: 1391; author reply 1392-1393.
10. Hauck WW, Anderson S, Marcus SM, (1998) Should we adjust for covariates in nonlinear regression analyses of randomized trials? Control Clin Trials 19: 249-256.
11. Hernandez AV, Steyerberg EW, Habbema JD, (2004) Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. J Clin Epidemiol 57: 454-460.
12. Ciolino J, Zhao W, Martin R, Palesch Y, (2011) Quantifying the cost in power of ignoring continuous covariate imbalances in clinical trial randomization. Contemp Clin Trials 32: 250-259.
13. Yu LM, Chan AW, Hopewell S, Deeks JJ, Altman DG, (2010) Reporting on covariate adjustment in randomised controlled trials before and after revision of the 2001 CONSORT statement: a literature review. Trials 11: 59.
14. Gail MH, Weiand S, Piantadosi S, (1984) Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 71: 431-444.
15. Hauck WW, Neuhaus JM, Kalbfleisch JD, Anderson S, (1991) A consequence of omitted covariates when estimating odds ratios. J Clin Epidemiol 44: 77-81.
16. Greenland S, Robins JM, Pearl J, (1999) Confounding and Collapsibility in Causal Inference. Statistical Science 14: 29-46.
17. International Conference on Harmonisation E9 Expert Working Group (1999) ICH Harmonised Tripartite Guideline. Statistical principles for clinical trials. Stat Med 18: 1905-1942.
18. Committee for Proprietary Medicinal Products (CPMP) (2004) Committee for Proprietary Medicinal Products (CPMP): points to consider on adjustment for baseline covariates. Stat Med 23: 701-709.
19. Altman DG, Schulz KF, Moher D, Egger M, Davidoff F, et al. (2001) The revised CONSORT statement for reporting randomized trials: explanation and elaboration. Ann Intern Med 134: 663-694.
20. Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB, (2010) A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. J Clin Epidemiol 63: 142-153.
21. Pocock SJ, Assmann SE, Enos LE, Kasten LE, (2002) Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med 21: 2917-2930.
22. Keen HI, Pile K, Hill CL, (2005) The prevalence of underpowered randomized clinical trials in rheumatology. J Rheumatol 32: 2083-2088.
23. Moher D, Dulberg CS, Wells GA, (1994) Statistical power, sample size, and their reporting in randomized controlled trials. JAMA 272: 122-1244.
24. Dickinson K, Bunn F, Wentz R, Edwards P, Roberts I, (2000) Size and quality of randomised controlled trials in head injury: review of published studies. BMJ 320: 1308-1311.
25. Chan AW, Altman DG, (2005) Epidemiology and reporting of randomised trials published in PubMed journals. Lancet 365: 1159-1162.
26. Charles P, Giraudeau B, Dechartres A, Baron G, Ravaud P, (2009) Reporting of sample size calculation in randomised controlled trials: Review. BMJ 2009; 338: b1732.
27. Zelen M, (1974) The randomization and stratification of patients to clinical trials. J Chronic Dis 27: 365-375.
28. Walter SD, (1985) Small sample estimation of log odds ratios from logistic regression and fourfold tables. Stat Med 4: 437-444.
29. Flury BK, Riedwyl H, (1986) Standard distance in univariate and multivariate analysis. The American Statistician 40: 249-251.
30. Rosenbaum PR, Rubin DB, (1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 39: 33-38.
31. Cui L, Hung HM, Wang SJ, Tsong Y, (2002) Issues related to subgroup analysis in clinical trials. J Biopharm Stat 12: 347-358.
32. Negassa A, Hanley JA, (2007) The effect of omitted covariates on confidence interval and study power in binary outcome analysis: a simulation study. Contemp Clin Trials 28: 242-248.
33. Dodge Y, (2003) The Oxford Dictionary of Statistical Terms. 6th ed New York Oxford University Press.
34. Rosenbaum PR, Rubin DB, (1984) Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79: 516-524.
35. Guyatt GH, Oxman AD, Schunemann HJ, Tugwell P, Knottnerus A, (2011) GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol 64: 380-382.