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      Bayesian imperfect information analysis for clinical recurrent data

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          Abstract

          In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our methods illustrated with an analysis of imperfect information from a trial of immunotherapy in the treatment of chronic granulomatous disease. In addition, we present evidence toward a better understanding of the differing behaviors along with concomitant variables. Based on the results of simulations, the imperfect information value of the concomitant variables was evaluated and different realistic situations were compared to see which could yield more accurate results for medical decision-making.

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          Most cited references 26

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          Survival analysis for recurrent event data: an application to childhood infectious diseases.

          Many extensions of survival models based on the Cox proportional hazards approach have been proposed to handle clustered or multiple event data. Of particular note are five Cox-based models for recurrent event data: Andersen and Gill (AG); Wei, Lin and Weissfeld (WLW); Prentice, Williams and Peterson, total time (PWP-CP) and gap time (PWP-GT); and Lee, Wei and Amato (LWA). Some authors have compared these models by observing differences that arise from fitting the models to real and simulated data. However, no attempt has been made to systematically identify the components of the models that are appropriate for recurrent event data. We propose a systematic way of characterizing such Cox-based models using four key components: risk intervals; baseline hazard; risk set, and correlation adjustment. From the definitions of risk interval and risk set there are conceptually seven such Cox-based models that are permissible, five of which are those previously identified. The two new variant models are termed the 'total time - restricted' (TT-R) and 'gap time - unrestricted' (GT-UR) models. The aim of the paper is to determine which models are appropriate for recurrent event data using the key components. The models are fitted to simulated data sets and to a data set of childhood recurrent infectious diseases. The LWA model is not appropriate for recurrent event data because it allows a subject to be at risk several times for the same event. The WLW model overestimates treatment effect and is not recommended. We conclude that PWP-GT and TT-R are useful models for analysing recurrent event data, providing answers to slightly different research questions. Further, applying a robust variance to any of these models does not adequately account for within-subject correlation. Copyright 2000 John Wiley & Sons, Ltd.
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            Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test.

            Planning studies involving diagnostic tests is complicated by the fact that virtually no test provides perfectly accurate results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be taken into account, whether the primary goal of the study is to estimate the prevalence of a disease in a population or to investigate the properties of a new diagnostic test. Previous work on sample size requirements for estimating the prevalence of disease in the case of a single imperfect test showed very large discrepancies in size when compared to methods that assume a perfect test. In this article we extend these methods to include two conditionally independent imperfect tests, and apply several different criteria for Bayesian sample size determination to the design of such studies. We consider both disease prevalence studies and studies designed to estimate the sensitivity and specificity of diagnostic tests. As the problem is typically nonidentifiable, we investigate the limits on the accuracy of parameter estimation as the sample size approaches infinity. Through two examples from infectious diseases, we illustrate the changes in sample sizes that arise when two tests are applied to individuals in a study rather than a single test. Although smaller sample sizes are often found in the two-test situation, they can still be prohibitively large unless accurate information is available about the sensitivities and specificities of the tests being used.
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              True versus Apparent Malaria Infection Prevalence: The Contribution of a Bayesian Approach

              Aims To present a new approach for estimating the “true prevalence” of malaria and apply it to datasets from Peru, Vietnam, and Cambodia. Methods Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia. Results Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the “true” estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence. Conclusions Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives.
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                Author and article information

                Journal
                Ther Clin Risk Manag
                Ther Clin Risk Manag
                Therapeutics and Clinical Risk Management
                Therapeutics and Clinical Risk Management
                Dove Medical Press
                1176-6336
                1178-203X
                2015
                19 December 2014
                : 11
                : 17-26
                Affiliations
                [1 ]Department of Cardiology, Jen-Ai Hospital, Dali District, Taichung, Taiwan
                [2 ]School of Medical Informatics, Chung Shan Medical University, Information Technology Office of Chung Shan Medical University Hospital, Taichung, Taiwan
                Author notes
                Correspondence: Chi-Chang Chang, School of Medical Informatics, Chung Shan Medical University, and Information Technology Office of Chung Shan Medical University Hospital, Taichung, Taiwan, Email changintw@ 123456gmail.com
                Article
                tcrm-11-017
                10.2147/TCRM.S67011
                4278741
                © 2015 Chang and Chang. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                Categories
                Original Research

                Medicine

                chronic granulomatous disease, recurrent events, bayesian value-of-information

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