As blinding of patients, investigators and the trial team is important in clinical trials to avoid bias (see, e.g. This estimator requires unblinding of the treatment group at the time of the interim analysis. Wittes and Brittain, 1990 Birkett and Day, 1994 Coffey and Muller, 1999 Denne and Jennison, 1999 Wittes et al., 1999 Zucker et al., 1999 Kieser and Friede, 2000 Coffey and Muller, 2001 Miller, 2005). One approach is the common pooled variance estimator that is often used for sample size re-estimation (see, e.g. Several approaches to estimate the variance in an ongoing trial have been suggested and their performance has been studied. Designs allowing a re-assessment of the initial sample size during an ongoing trial have become increasingly popular. However, situations occur where these parameters cannot be estimated or can be estimated only with considerable uncertainty at the planning stage of the trial. While the significance level and power are set by the researcher, the other parameters are usually estimates obtained from previous trials. This sample size usually depends on a specified significance level and power but also on other parameters such as variances, mean values, or response rates. The traditional approach to conducting a confirmatory clinical trial is to calculate a fixed sample size in advance of the study. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups.
Simulation results show that the naïve (one-sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Their performance with respect to bias and standard error is compared to the unblinded estimator. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. These methods usually focus on estimating the variance. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Misspecification of these parameters can lead to under- or overestimation of the sample size. However, the sample size quite often depends on parameters that might not be known in advance of the study. Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial.
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