Madhu Mazumdar1. 1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E. 63rd St., 3rd floor, New York, NY 10021, USA. mazumdar@biost.mskcc.org
Abstract
PURPOSE: Comparative diagnostic accuracy (CDA) studies are typically small retrospective studies supporting a higher accuracy for one modality over another for either staging a particular disease or assessing response to therapy, and they are used to generate hypotheses for larger prospective trials. The purpose of this article is to introduce the group sequential design (GSD) approach in planning these larger trials. METHODS: Methodology needed for using GSD in the CDA studies is recently developed. In this article, GSD with the O'Brien and Fleming (OBF) stopping rule is described and guidelines for sample size calculation are provided. Simulated data is used to demonstrate the application of GSD in the design/analysis of a clinical trial in the CDA study setting. RESULTS: The expected sample size needed for planning a trial with GSD (under the OBF stopping rule) is slightly inflated but may ultimately result in greater savings of patient resources. CONCLUSION: GSD is a specialized statistical method that is helpful in balancing the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion and should be adopted for planning CDA studies.
PURPOSE: Comparative diagnostic accuracy (CDA) studies are typically small retrospective studies supporting a higher accuracy for one modality over another for either staging a particular disease or assessing response to therapy, and they are used to generate hypotheses for larger prospective trials. The purpose of this article is to introduce the group sequential design (GSD) approach in planning these larger trials. METHODS: Methodology needed for using GSD in the CDA studies is recently developed. In this article, GSD with the O'Brien and Fleming (OBF) stopping rule is described and guidelines for sample size calculation are provided. Simulated data is used to demonstrate the application of GSD in the design/analysis of a clinical trial in the CDA study setting. RESULTS: The expected sample size needed for planning a trial with GSD (under the OBF stopping rule) is slightly inflated but may ultimately result in greater savings of patient resources. CONCLUSION:GSD is a specialized statistical method that is helpful in balancing the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion and should be adopted for planning CDA studies.