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Zapf A, Stark M, Gerke O, Ehret C, Benda N, Bossuyt P, Deeks J, Reitsma J, Alonzo T, Friede T. Adaptive trial designs in diagnostic accuracy research. Stat Med 2019; 39:591-601. [PMID: 31773788 DOI: 10.1002/sim.8430] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 10/23/2019] [Accepted: 10/26/2019] [Indexed: 11/10/2022]
Abstract
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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Affiliation(s)
- Antonia Zapf
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maria Stark
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Oke Gerke
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | | | - Norbert Benda
- Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany.,Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Patrick Bossuyt
- Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, The Netherlands
| | - Jon Deeks
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.,NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Trust and the University of Birmingham, Birmingham, UK
| | - Johannes Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht & University Utrecht, Utrecht, The Netherlands
| | - Todd Alonzo
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
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2
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Lu D, Zhou C, Tang L, Tan M, Yuan A, Chan L. Evaluating accuracy of diagnostic tests without conditional independence assumption. Stat Med 2018; 37:2809-2821. [PMID: 29691895 DOI: 10.1002/sim.7688] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 03/01/2018] [Accepted: 03/23/2018] [Indexed: 11/09/2022]
Abstract
Evaluating the accuracy (ie, estimating the sensitivity and specificity) of new diagnostic tests without the presence of a gold standard is of practical meaning and has been the subject of intensive study for several decades. Existing methods use 2 or more diagnostic tests under several basic assumptions and then estimate the accuracy parameters via the maximum likelihood estimation. One of the basic assumptions is the conditional independence of the tests given the disease status. This assumption is impractical in many real applications in veterinary research. Several methods have been proposed with various dependence models to relax this assumption. However, these methods impose subjective dependence structures, which may not be practical and may introduce additional nuisance parameters. In this article, we propose a simple method for addressing this problem without the conditional independence assumption, using an empirical conditioning approach. The proposed method reduces to the popular Hui-Walter model in the case of conditional independence. Also, our likelihood function is of order-2 polynomial in parameters, while that of Hui-Walter is of order-3. The reduced model complexity increases the stability in estimation. Simulation studies are conducted to evaluate the performance of the proposed method, which shows overall smaller biases in estimation and is more stable than the existing method, especially when tests are conditionally dependent. Two real data examples are used to illustrate the proposed method.
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Affiliation(s)
- Di Lu
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 20057, Washington,DC, USA
| | - Chunxiao Zhou
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Larry Tang
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, 20892, MD, USA
- Department of Statistics, George Mason University, Fairfax, 22030, VA, USA
| | - Ming Tan
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 20057, Washington,DC, USA
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 20057, Washington,DC, USA
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Leighton Chan
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, 20892, MD, USA
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Zhang J, Zhang Y, Chaloner K, Stapleton JT. A sequential classification rule based on multiple quantitative tests in the absence of a gold standard. Stat Med 2015; 35:1359-72. [PMID: 26522690 DOI: 10.1002/sim.6780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 09/20/2015] [Accepted: 10/06/2015] [Indexed: 12/19/2022]
Abstract
In many medical applications, combining information from multiple biomarkers could yield a better diagnosis than any single one on its own. When there is a lack of a gold standard, an algorithm of classifying subjects into the case and non-case status is necessary for combining multiple markers. The aim of this paper is to develop a method to construct a composite test from multiple applicable tests and derive an optimal classification rule under the absence of a gold standard. Rather than combining the tests, we treat the tests as a sequence. This sequential composite test is based on a mixture of two multivariate normal latent models for the distribution of the test results in case and non-case groups, and the optimal classification rule is derived returning the greatest sensitivity at a given specificity. This method is applied to a real-data example and simulation studies have been carried out to assess the statistical properties and predictive accuracy of the proposed composite test. This method is also attainable to implement nonparametrically.
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Affiliation(s)
- Jingyang Zhang
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
| | - Ying Zhang
- Department of Biostatistics, Indiana University Fairbanks School of Public Health and School of Medicine, Indianapolis, IN 46202, U.S.A.,Department of Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kathryn Chaloner
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, U.S.A.,Department of Statistics and Actuarial Sciences, University of Iowa, Iowa City, IA 52242, U.S.A
| | - Jack T Stapleton
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, U.S.A
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