Sequential diagnostic trial designs

Tang, Lin

doi:10.1002/wics.136

Cited by 4 publications

(2 citation statements)

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“…Regarding group sequential designs in AUC studies without sample size reestimation or other modifications, there are several articles (for an overview see for example). To our knowledge, the first article about group sequential designs in diagnostic research was written by Mazumdar and Liu, in which the authors propose an approach based on a binormal distribution (transferable to other distributions or nonparametric models) for the comparison of two AUCs …”

Section: Adaptive Designs For Diagnostic Accuracy Studiesmentioning

confidence: 99%

Adaptive trial designs in diagnostic accuracy research

Zapf

Stark

Gerke

et al. 2019

Statistics in Medicine

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The aim of diagnostic accuracy studies is to evaluate how accurately a diagnostic test can distinguish diseased from nondiseased individuals. Depending on the research question, different study designs and accuracy measures are appropriate. As the prior knowledge in the planning phase is often very limited, modifications of design aspects such as the sample size during the ongoing trial could increase the efficiency of diagnostic trials. In intervention studies, group sequential and adaptive designs are well established. Such designs are characterized by preplanned interim analyses, giving the opportunity to stop early for efficacy or futility or to modify elements of the study design. In contrast, in diagnostic accuracy studies, such flexible designs are less common, even if they are as important as for intervention studies. However, diagnostic accuracy studies have specific features, which may require adaptations of the statistical methods or may lead to specific advantages or limitations of sequential and adaptive designs.In this article, we summarize the current status of methodological research and applications of flexible designs in diagnostic accuracy research. Furthermore, we indicate and advocate future development of adaptive design methodology and their use in diagnostic accuracy trials from an interdisciplinary viewpoint. The term "interdisciplinary viewpoint" describes the collaboration of experts of the academic and nonacademic research.

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Section: Adaptive Designs For Diagnostic Accuracy Studiesmentioning

confidence: 99%

Adaptive trial designs in diagnostic accuracy research

Zapf

Stark

Gerke

et al. 2019

Statistics in Medicine

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“…The notion of an organizational dimension has been used in latest projects on cognitive organizations. Dong and Tang [1] discovered a number of "general activities," such as sequential diagnostic and regular construction, for which task-specific description language and regulatory systems were developed. Nurmayanti, Jumadi, Wilujeng and Kuswanto [2] have developed a number of technical expertise systems based on frameworks that combine cognitive learning instruments with problemsolving techniques Adamski and Saeed [3] have described the HERACLES design's heuristic classification method.…”

Section: Introductionmentioning

confidence: 99%

Knowledge and Information Management Tools in Architectural Dimensions

Mr¹

2021

JBSHA

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A knowledge systems design is a layer of learning modeling approach that focuses on applying generic Artificial Intelligence (AI) application approaches to a specific category of problem-solving activities. The consequences of 3 major approaches of the architectural dimension for the development of Knowledge Engineering (KE) tools are discussed in this study. The methodology is shown by a hierarchy of KE tools to enable systems management and knowledge development at the architectural design dimension, as well as architecture for controlling uncertainty through reasoning about actions. This article discusses architecture-dimension tools for KE. Knowledge architecture is a way of tailoring conventional AI problem-solving strategies to a specific set of activities. Architecture describes a certain kind of issue solution (e.g., diagnostic or reconfiguration) at a theoretical dimension above the application, indicating which parts of a problem class are fundamental to the issue and which are integration artifacts. An information system's design is a partial model in which certain choices are taken ahead of time to accommodate specific job characteristics. Many medical diagnostic systems, for example, evaluate data from the bottom up to identify "triggered" illness hypotheses, and then create top-down tasks to gather evidence for and against the hypotheses. Although it may be executed in a number of ways, the "trigger and acquire proof" cycle is an essential aspect of any design for the domain of clinical imaging activities.

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