Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis

Brennan C Kahan, Tim P Morris, Brennan C Kahan, Tim P Morris

Abstract

Objectives: To assess how often stratified randomisation is used, whether analysis adjusted for all balancing variables, and whether the method of randomisation was adequately reported, and to reanalyse a previously reported trial to assess the impact of ignoring balancing factors in the analysis.

Design: Review of published trials and reanalysis of a previously reported trial.

Setting: Four leading general medical journals (BMJ, Journal of the American Medical Association, Lancet, and New England Journal of Medicine) and the second Multicenter Intrapleural Sepsis Trial (MIST2).

Participants: 258 trials published in 2010 in the four journals. Cluster randomised, crossover, non-randomised, single arm, and phase I or II trials were excluded, as were trials reporting secondary analyses, interim analyses, or results that had been previously published in 2010.

Main outcome measures: Whether the method of randomisation was adequately reported, how often balanced randomisation was used, and whether balancing factors were adjusted for in the analysis.

Results: Reanalysis of MIST2 showed that an unadjusted analysis led to larger P values and a loss of power. The review of published trials showed that balanced randomisation was common, with 163 trials (63%) using at least one balancing variable. The most common methods of balancing were stratified permuted blocks (n=85) and minimisation (n=27). The method of randomisation was unclear in 37% of trials. Most trials that balanced on centre or prognostic factors were not adequately analysed; only 26% of trials adjusted for all balancing factors in their primary analysis. Trials that did not adjust for balancing factors in their analysis were less likely to show a statistically significant result (unadjusted 57% v adjusted 78%, P=0.02).

Conclusion: Balancing on centre or prognostic factors is common in trials but often poorly described, and the implications of balancing are poorly understood. Trialists should adjust their primary analysis for balancing factors to obtain correct P values and confidence intervals and to avoid an unnecessary loss in power.

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4790498/bin/kahb003399.f1_default.jpg
Fig 1 Correlation in mean survival time between treatment groups under simple and stratified randomisation (simulated data). Data were generated from the formula: survival time=3months+(6months)×(early disease stage)+random error, where random error ~ N(0, 1)
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4790498/bin/kahb003399.f2_default.jpg
Fig 2 Flow diagram of study selection

References

    1. Altman DG, Bland JM. Treatment allocation by minimisation. BMJ 2005;330:843.
    1. Matts JP, Lachin JM. Properties of permuted-block randomization in clinical trials. Control Clin Trials 1988;9:327-44.
    1. ICH Harmonised Tripartite Guideline. Statistical principles for clinical trials. International Conference on Harmonisation E9 Expert Working Group. Stat Med 1999;18:1905-42.
    1. Kahan BC, Morris TP. Improper analysis of trials randomised using stratified blocks or minimisation. Stat Med 2012;31:328-40.
    1. Kernan WN, Viscoli CM, Makuch RW, Brass LM, Horwitz RI. Stratified randomization for clinical trials. J Clin Epidemiol 1999;52:19-26.
    1. Parzen M, Lipsitz SR, Dear KBG. Does clustering affect the usual test statistics of no treatment effect in a randomized clinical trial?. Biom J 1998;40:385-402.
    1. Raab GM, Day S, Sales J. How to select covariates to include in the analysis of a clinical trial. Control Clin Trials 2000;21:330-42.
    1. Scott NW, McPherson GC, Ramsay CR, Campbell MK. The method of minimization for allocation to clinical trials. a review. Control Clin Trials 2002;23:662-74.
    1. Moher D, Hopewell S, Schulz KF, Montori V, Gotzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 2010;340:c869.
    1. Rahman NM, Maskell NA, West A, Teoh R, Arnold A, Mackinlay C, et al. Intrapleural use of tissue plasminogen activator and DNase in pleural infection. N Engl J Med 2011;365:518-26.
    1. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 2000;355:1064-9.
    1. Hewitt CE, Torgerson DJ. Is restricted randomisation necessary? BMJ 2006;332:1506-8.
    1. Hernandez AV, Steyerberg EW, Habbema JD. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. J Clin Epidemiol 2004;57:454-60.
    1. Turner EL, Perel P, Clayton T, Edwards P, Hernandez AV, Roberts I, et al. Covariate adjustment increased power in randomized controlled trials: an example in traumatic brain injury. J Clin Epidemiol 2012;65:474-81.
    1. Kennes LN, Cramer E, Hilgers RD, Heussen N. The impact of selection bias on test decisions in randomized clinical trials. Stat Med 2011;30:2573-81.
    1. Schulz KF, Grimes DA. Unequal group sizes in randomised trials: guarding against guessing. Lancet 2002;359:966-70.
    1. Plint AC, Moher D, Morrison A, Schulz K, Altman DG, Hill C, et al. Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review. Med J Aust 2006;185:263-7.

Source: PubMed

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