Accessibility navigation


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

Kunz, C. U., Stallard, N., Parsons, N., Todd, S. ORCID: https://orcid.org/0000-0002-9981-923X and Friede, T. (2017) Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations. Biometrical Journal, 59 (2). pp. 344-357. ISSN 0323-3847

[img]
Preview
Text (Open access (Version of Record online)) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

1MB
[img] Text - Accepted Version
· Restricted to Repository staff only

536kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/bimj.201500233

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:65973
Publisher:Wiley

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation