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Qiu SF, Zhang XL, Qu YQ, Han YQ. Multiple test procedures of disease prevalence based on stratified partially validated series in the presence of a gold standard. J Biopharm Stat 2024; 34:753-774. [PMID: 37853747 DOI: 10.1080/10543406.2023.2269262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/05/2023] [Indexed: 10/20/2023]
Abstract
This paper discusses the problem of disease prevalence in clinical studies, focusing on multiple comparisons based on stratified partially validated series in the presence of a gold standard. Five test statistics, including two Wald-type test statistics, the inverse hyperbolic tangent transformation test statistic, likelihood ratio test statistic, and score test statistic, are proposed to conduct multiple comparisons. To control the overall type I error rate, several adjustment procedures are developed, namely the Bonferroni, Single-step adjusted MaxT, Single-step adjusted MinP, Holm's Step-down, and Hochberg's step-up procedures, based on these test statistics. The performance of the proposed methods is evaluated through simulation studies in terms of the empirical type I error rate and empirical power. Simulation results show that the Single-step adjusted MaxT procedure and Single-step adjusted MinP procedure generally outperform the other three procedures, and these two test procedures based on all test statistics have satisfactory performance. Notably, the Single-step adjusted MinP procedure tends to exhibit higher empirical power than the Single-step adjusted MaxT procedure. Furthermore, the Step-down and Step-up procedures show greater power compared to the Bonferroni method. The study also observes that as the validated ratio increases, the empirical type I errors of all test procedures approach the nominal level while maintaining higher power. Two real examples are presented to illustrate the proposed methods.
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Affiliation(s)
- Shi-Fang Qiu
- Department of Statistics and Data Science, Chongqing University of Technology, Chongqing, China
| | - Xiao-Liang Zhang
- Department of Statistics and Data Science, Chongqing University of Technology, Chongqing, China
- School of Iflytfk Data Science, Chongqing City Vocational College, Chongqing, China
| | - Ying-Qiu Qu
- Department of Statistics and Data Science, Chongqing University of Technology, Chongqing, China
- School of Finance, Chongqing College of Finance and Economics, Chongqing, China
| | - Yuan-Quan Han
- Department of Statistics and Data Science, Chongqing University of Technology, Chongqing, China
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Al-Kandari NM, Garthwaite PH. Bayesian analysis of misclassified binomial data: double-sampling and the zero-numerator problem. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1855448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Noriah M. Al-Kandari
- Department of Statistics and Operations Research, Kuwait University, Safat, Kuwait
| | - Paul H. Garthwaite
- School of Mathematics and Statistics, The Open University, Milton Keynes, UK
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3
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Qiu SF, Fu QX. Homogeneity testing for binomial proportions under stratified double-sampling scheme with two fallible classifiers. Stat Methods Med Res 2020; 29:3547-3568. [PMID: 32640937 DOI: 10.1177/0962280220932601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article investigates the homogeneity testing problem of binomial proportions for stratified partially validated data obtained by double-sampling method with two fallible classifiers. Several test procedures, including the weighted-least-squares test with/without log-transformation, logit-transformation and double log-transformation, and likelihood ratio test and score test, are developed to test the homogeneity under two models, distinguished by conditional independence assumption of two classifiers. Simulation results show that score test performs better than other tests in the sense that the empirical size is generally controlled around the nominal level, and hence be recommended to practical applications. Other tests also perform well when both binomial proportions and sample sizes are not small. Approximate sample sizes based on score test, likelihood ratio test and the weighted-least-squares test with double log-transformation are generally accurate in terms of the empirical power and type I error rate with the estimated sample sizes, and hence be recommended. An example from the malaria study is illustrated by the proposed methodologies.
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Affiliation(s)
- Shi-Fang Qiu
- Department of Statistics, Chongqing University of Technology, Chongqing, China
| | - Qi-Xiang Fu
- Department of Statistics, Chongqing University of Technology, Chongqing, China
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Qiu SF, He J, Tao JR, Tang ML, Poon WY. Comparison of disease prevalence in two populations under double-sampling scheme with two fallible classifiers. J Appl Stat 2019; 47:1375-1401. [DOI: 10.1080/02664763.2019.1679727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Shi-Fang Qiu
- Department of Statistics, Chongqing University of Technology, Chongqing, People's Republic of China
| | - Jie He
- Department of Statistics, Chongqing University of Technology, Chongqing, People's Republic of China
| | - Ji-Ran Tao
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Man-Lai Tang
- Department of Mathematics and Statistics, Hang Seng University of Hong Kong, Hong Kong, People's Republic of China
| | - Wai-Yin Poon
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, People's Republic of China
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Qiu SF, Zeng XS, Tang ML, Poon WY. Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers. Stat Methods Med Res 2017; 28:1019-1043. [PMID: 29233082 DOI: 10.1177/0962280217744239] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example.
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Affiliation(s)
- Shi-Fang Qiu
- 1 Department of Statistics, Chongqing University of Technology, Chongqing, China
| | - Xiao-Song Zeng
- 1 Department of Statistics, Chongqing University of Technology, Chongqing, China
| | - Man-Lai Tang
- 2 Department of Mathematics and Statistics, Hang Seng Management College, Hong Kong, China
| | - Wai-Yin Poon
- 3 Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
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Qiu SF, Lian H, Zou GY, Zeng XS. Interval estimation for a proportion using a double-sampling scheme with two fallible classifiers. Stat Methods Med Res 2016; 27:2478-2503. [DOI: 10.1177/0962280216681599] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Double-sampling schemes using one classifier assessing the whole sample and another classifier assessing a subset of the sample have been introduced for reducing classification errors when an infallible or gold standard classifier is unavailable or impractical. Inference procedures have previously been proposed for situations where an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Here, we consider the case where both classifiers are fallible, proposing and evaluating several confidence interval procedures for a proportion under two models, distinguished by the assumption regarding ascertainment of two classifiers. Simulation results suggest that the modified Wald-based confidence interval, Score-based confidence interval, two Bayesian credible intervals, and the percentile Bootstrap confidence interval performed reasonably well even for small binomial proportions and small validated sample under the model with the conditional independent assumption, and the confidence interval derived from the Wald test with nuisance parameters appropriately evaluated, likelihood ratio-based confidence interval, Score-based confidence interval, and the percentile Bootstrap confidence interval performed satisfactory in terms of coverage under the model without the conditional independent assumption. Moreover, confidence intervals based on log- and logit-transformations also performed well when the binomial proportion and the ratio of the validated sample are not very small under two models. Two examples were used to illustrate the procedures.
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Affiliation(s)
- Shi-Fang Qiu
- Department of Statistics, Chongqing University of Technology, Chongqing, China
| | - Heng Lian
- Department of Mathematics, City University of Hong Kong, Kowloon Tong, Hong Kong
| | - GY Zou
- Department of Epidemiology and Biostatistics, Robarts Clinical Trials of Robarts Research Institute, Western University, Ontario, Canada
| | - Xiao-Song Zeng
- Department of Statistics, Chongqing University of Technology, Chongqing, China
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Qiu SF, Poon WY, Tang ML. Confidence intervals for proportion difference from two independent partially validated series. Stat Methods Med Res 2016; 25:2250-2273. [DOI: 10.1177/0962280213519718] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Partially validated series are common when a gold-standard test is too expensive to be applied to all subjects, and hence a fallible device is used accordingly to measure the presence of a characteristic of interest. In this article, confidence interval construction for proportion difference between two independent partially validated series is studied. Ten confidence intervals based on the method of variance estimates recovery (MOVER) are proposed, with each using the confidence limits for the two independent binomial proportions obtained by the asymptotic, Logit-transformation, Agresti–Coull and Bayesian methods. The performances of the proposed confidence intervals and three likelihood-based intervals available in the literature are compared with respect to the empirical coverage probability, confidence width and ratio of mesial non-coverage to non-coverage probability. Our empirical results show that (1) all confidence intervals exhibit good performance in large samples; (2) confidence intervals based on MOVER combining the confidence limits for binomial proportions based on Wilson, Agresti–Coull, Logit-transformation, Bayesian (with three priors) methods perform satisfactorily from small to large samples, and hence can be recommended for practical applications. Two real data sets are analysed to illustrate the proposed methods.
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Affiliation(s)
- Shi-Fang Qiu
- Department of Statistics, Chongqing University of Technology, Chongqing, China
| | - Wai-Yin Poon
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
| | - Man-Lai Tang
- Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
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Confidence limits for prevalence of disease adjusted for estimated sensitivity and specificity. Prev Vet Med 2014; 113:13-22. [PMID: 24416798 DOI: 10.1016/j.prevetmed.2013.09.015] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Prevalence of a disease is usually assessed by diagnostic tests that may produce false results. Rogan and Gladen (1978) described a method to estimate the true prevalence correcting for sensitivity and specificity of the diagnostic procedure, and Reiczigel et al. (2010) provided exact confidence intervals for the true prevalence assuming sensitivity and specificity were known. In this paper we propose a new method to construct approximate confidence intervals for the true prevalence when sensitivity and specificity are estimated from independent samples. To improve coverage we applied an adjustment similar to that described in Agresti and Coull (1998). According to an extensive simulation study the new confidence intervals maintain the nominal level fairly well even for sample sizes as small as 30; mini-mum coverage is above 88%, 93%, and 98% at nominal 90%, 95%, and 99%, respectively. We illustrate the advantages of the proposed method with real-life applications.
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Comparison of disease prevalence in two populations in the presence of misclassification. Biom J 2012; 54:786-807. [DOI: 10.1002/bimj.201100216] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Revised: 05/22/2012] [Accepted: 07/19/2012] [Indexed: 11/07/2022]
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10
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Mittlböck M, Edler L, LeBlanc M, Niland J, Zwinderman K. Second Issue for Computational Statistics for Clinical Research. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2012.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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