Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

Abstract

Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under‐ or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re‐assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. 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. Their performance with respect to bias and standard error is compared to 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. 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. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups.

Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity with A Self-Paced Brain-Computer Interface

“…(c) What was the difference in these effects across the three paradigms? We evaluated (a) in terms of absolute units (mV) using blinded covariate analysis [ 43 ], and (b), (c) in terms of both absolute units and relative units (% change). For absolute units, we computed the peak-peak amplitudes of the MEPs and averaged them across the 15 trials.…”

Sample size calculation in clinical trials with two co‐primary endpoints including overdispersed count and continuous outcomes

“…Further, a blinded estimation of the correlation coefficient based on interim data at the midcourse of a trial can be a suitable solution to avoid an underpowered or overpowered trial. 33 Nonetheless, this method has been developed for a trial to compare continuous endpoints. Therefore, further extensions of methods proposed for two co-primary endpoints, including overdispersed count and continuous outcomes, are needed.…”

“…In these situations, rough correlation values must be estimated by using other metrics (such as coefficient of determination) from a systematic review of medical literature, as shown in Section 4. Further, a blinded estimation of the correlation coefficient based on interim data at the midcourse of a trial can be a suitable solution to avoid an underpowered or overpowered trial 33 . Nonetheless, this method has been developed for a trial to compare continuous endpoints.…”

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment

“…Existing work in this context has mainly focused on trials with multiple endpoints. [14] approached the sample size calculation of trials with multiple, correlated endpoints. They proposed estimators for the covariance and the correlation based on blinded data obtained at an interim analysis.…”