gms | German Medical Science

65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

06.09. - 09.09.2020, Berlin (online conference)

Operating characteristic guided design of group-sequential trials

Meeting Abstract

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  • Aniko Szabo - Medical College of Wisconsin, Milwaukee, WI, United States
  • Sergey Tarima - Medical College of Wisconsin, Milwaukee, WI, United States

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS). Berlin, 06.-09.09.2020. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 380

doi: 10.3205/20gmds040, urn:nbn:de:0183-20gmds0403

Veröffentlicht: 26. Februar 2021

© 2021 Szabo et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Group-sequential designs are commonly used for clinical trials to allow early stopping for efficacy or futility. While the design of a single-stage randomized trial is guided by a target power for an alternative hypothesis of interest, the addition of interim analyses is driven by technical choices that are less understandable for clinicians. For example, the commonly used Lan-DeMets methodology requires specification of the timing of analyses and error spending functions. Since the rationale and effect of these technical choices is often unclear, the operating characteristics of the final design are explored under various values of the parameter of interest, and the design is then adjusted until desired properties are obtained.

In this work we develop methods for constructing designs that achieve the desired operating characteristics without the need to specify error spending functions or the timing of analyses. Specifically, we consider designing a study for the mean difference δ of a normally distributed outcome with known variance. The null hypothesis H0: δ=δ0 is tested versus Ha: δ=δA, with power π at a significance level α. The interim analyses are designed so that for a pre-specified sequence δAk the study stops for efficacy at stage k with probability π if δ=δAk. If stopping for futility is also considered, then the requirement to stop for futility at stage k with probability πF if δ=δ0k for pre-specified sequence δ0k can also be added.

We show that under some monotonicity restrictions, such designs exist for any choice of the timing of interim analyses. Specific designs can be selected by imposing additional optimality requirements, such as minimizing the expected sample size under the target alternative δA, or the average sample size under a weighted selection of the alternatives.

The utility of the proposed methods is demonstrated via a simulation study and illustrated examples of prior trials. The methods are implemented in the R package gsDesignOC.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.