| Titre : | Comparison of balanced and random allocation in clinical trials : A simulation study. (2000) |
| Auteurs : | Maroeska-M ROVERS ; Huub STRAATMAN ; Gerhard-A ZIELHUIS ; Department of Epidemiology. University Medical Centre Nijmegen. NLD |
| Type de document : | Article |
| Dans : | European journal of epidemiology (vol. 16, n° 12, 2000) |
| Pagination : | 1123-1129 |
| Langues: | Anglais |
| Mots-clés : | Europe ; Homme ; Epidémiologie ; Validité ; Méthodologie ; Thérapeutique ; Analyse ; Pays Bas |
| Résumé : | [BDSP. Notice produite par INIST-CNRS gvR0xBSf. Diffusion soumise à autorisation]. Background : Before analysing the results of a randomised controlled clinical trial in which 200 children were balanced over five prognostic factors, we wanted to know the efficiency of balanced allocation compared to simple randomisation as well as the efficiency of adjusted as compared to unadjusted statistical analysis in small and large sample sizes. Methods : A simulation study with 1000 replications of each assignment was performed for both relatively large trials (n=100) and for small trials (n=20). Four options for the design and analysis were studied : (1) simple randomisation with simple univariate analysis, (2) simple randomisation with multivariate modelling, (3) balanced allocation with simple univariate analysis and (4) balanced allocation with multivariate modelling. In addition, we also considered the effect of an unmeasured covariable, which was either uncorrelated or correlated with another covariate. Results/conclusion : The simulations show that a combination of balanced allocation and multivariate analysis as compared to simple randomisation and multivariate analysis leads to more valid and precise treatment effects as well as to smaller confidence intervals, especially in small trials (n=20). Multivariate analysis with all known prognostic factors produces on average smaller Type I errors and Type II errors in balanced allocation compared to simple randomisation. If an unmeasured covariate is strongly correlated with another covariate the treatment effect is estimated more precisely as compared to an unmeasured covariate that is not correlate or less strongly correlated. |
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