Estimation of odds ratio from group testing data with misclassified exposure.
misclassified exposure
odds ratio
prevalence rates
pseudo-likelihood
sensitivity
specificity
validation data
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
revised:
21
08
2023
received:
16
09
2022
accepted:
17
09
2023
medline:
29
1
2024
pubmed:
29
1
2024
entrez:
29
1
2024
Statut:
ppublish
Résumé
For low prevalence disease, we consider estimation of the odds ratio for two specified groups of individuals using group testing data. Broadly the two groups may be classified as "the exposed" and "the unexposed." Often in observational studies, the exposure status is not correctly recorded. In addition, diagnostic tests are rarely completely accurate. The proposed model accounts for imperfect sensitivity and specificity of diagnostic tests along with the misclassification in the exposure status. For model identifiability, we make use of internal validation data, where a subsample of reasonably small size is selected from the original sample by simple random sampling without replacement. Pseudo-maximum likelihood method is employed for the estimation of the model parameters. The performance of group testing methodology is compared with individual testing for different parametric configurations. A limited data study related to COVID-19 prevalence is performed to illustrate the methodology.
Identifiants
pubmed: 38285402
doi: 10.1002/bimj.202200254
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2200254Informations de copyright
© 2024 Wiley-VCH GmbH.
Références
Bound, J., Brown, C., & Mathiowetz, N. (2001). Measurement error in survey data. In K. J. Arrow & M. D. Intriligator (Eds.), Handbook of econometrics (vol. 5, pp. 3705-3843). Elsevier.
Buonaccorsi, J. P. (2010). Measurement error: Models, methods, and applications. Chapman and Hall/CRC.
Chen, C. L., & Swallow, W. H. (1990). Using group testing to estimate a proportion, and to test the binomial model. Biometrics, 1035-1046.
Chen, P., Tebbs, J. M., & Bilder, C. R. (2009). Group testing regression models with fixed and random effects. Biometrics, 65(4), 1270-1278.
Chi, S. W., Zang, J. B., Mele, A., & Darnell, R. B. (2009). Ago hits-clip decodes MIRNA-MRNA interaction maps. Nature, 460(7254), 479.
Chiang, C.-L., & Reeves, W. C. (1962). Statistical estimation of virus infection rates in mosquito vector populations. American Journal of Hygiene, 75(3), 377-391.
Delaigle, A., & Hall, P. (2012). Nonparametric regression with homogeneous group testing data. The Annals of Statistics, 40(1), 131-158.
Delaigle, A., Hall, P., & Wishart, J. (2014). New approaches to nonparametric and semiparametric regression for univariate and multivariate group testing data. Biometrika, 101(3), 567-585.
Delaigle, A., & Meister, A. (2011). Nonparametric regression analysis for group testing data. Journal of the American Statistical Association, 106(494), 640-650.
Dorfman, R. (1943). The detection of defective members of large populations. The Annals of Mathematical Statistics, 14(4), 436-440.
Fletcher, J., Russell, A., & Butler, R. (1999). Seed-borne cucumber mosaic virus in New Zealand lentil crops: Yield effects and disease incidence. New Zealand Journal of Crop and Horticultural Science, 27(3), 197-204.
Gastwirth, J. L. (2000). The efficiency of pooling in the detection of rare mutations. The American Journal of Human Genetics, 67(4), 1036-1039.
Gastwirth, J. L., & Hammick, P. A. (1989). Estimation of the prevalence of a rare disease, preserving the anonymity of the subjects by group testing: Application to estimating the prevalence of aids antibodies in blood donors. Journal of Statistical Planning and Inference, 22(1), 15-27.
Gustafson, P. (2003). Measurement error and misclassification in statistics and epidemiology: Impacts and Bayesian adjustments. CRC Press.
Hardwick, J., Page, C., & Stout, Q. F. (1998). Sequentially deciding between two experiments for estimating a common success probability. Journal of the American Statistical Association, 93(444), 1502-1511.
Hepworth, G., & Watson, R. (2009). Debiased estimation of proportions in group testing. Journal of the Royal Statistical Society: Series C (Applied Statistics), 58(1), 105-121.
Hund, L., & Pagano, M. (2013). Estimating “HIV” prevalence from surveys with low individual consent rates: Annealing individual and pooled samples. Emerging Themes in Epidemiology, 10(1), 2.
Johnson, W. O., & Pearson, L. M. (1999). Dual screening. Biometrics, 55(3), 867-873.
Kim, H.-Y., Hudgens, M. G., Dreyfuss, J. M., Westreich, D. J., & Pilcher, C. D. (2007). Comparison of group testing algorithms for case identification in the presence of test error. Biometrics, 63(4), 1152-1163.
Lewis, J. L., Lockary, V. M., & Kobic, S. (2012). Cost savings and increased efficiency using a stratified specimen pooling strategy for Chlamydia trachomatis and Neisseria gonorrhoeae. Sexually Transmitted Diseases, 39(1), 46-48.
Liu, A., Liu, C., Zhang, Z., & Albert, P. S. (2012). Optimality of group testing in the presence of misclassification. Biometrika, 99(1), 245-251.
Remlinger, K. S., Hughes-Oliver, J. M., Young, S. S., & Lam, R. L. (2006). Statistical design of pools using optimal coverage and minimal collision. Technometrics, 48(1), 133-143.
Roy, S., & Banerjee, T. (2019). Estimation of log-odds ratio from group testing data using firth correction. Biometrical Journal, 61(3), 714-728.
Spiegelman, D., & Gray, R. (1991). Cost-efficient study designs for binary response data with Gaussian covariate measurement error. Biometrics, 47(3), 851-869.
Spiegelman, D., Rosner, B., & Logan, R. (2000). Estimation and inference for logistic regression with covariate misclassification and measurement error in main study/validation study designs. Journal of the American Statistical Association, 95(449), 51-61.
Swallow, W. H. (1985). Group testing for estimating infection rates and probabilities of disease transmission. Phytopathology (USA), 75(1), 882-889.
Tu, X. M., Litvak, E., & Pagano, M. (1995). On the informativeness and accuracy of pooled testing in estimating prevalence of a rare disease: Application to HIV screening. Biometrika, 82(2), 287-297.
Van, T. T., Miller, J., Warshauer, D. M., Reisdorf, E., Jernigan, D., Humes, R., & Shult, P. A. (2012). Pooling nasopharyngeal/throat swab specimens to increase testing capacity for influenza viruses by PCR. Journal of Clinical Microbiology, 50(3), 891-896.
Vansteelandt, S., Goetghebeur, E., & Verstraeten, T. (2000). Regression models for disease prevalence with diagnostic tests on pools of serum samples. Biometrics, 56(4), 1126-1133.
Walter, S. D., Hildreth, S. W., & Beaty, B. J. (1980). Estimation of infection rates in populations of organisms using pools of variable size. American Journal of Epidemiology, 112(1), 124-128.
Wang, D., & Gustafson, P. (2014). On the impact of misclassification in an ordinal exposure variable. Epidemiologic Methods, 3(1), 97-106.
Wang, D., Zhou, H., & Kulasekera, K. (2013). A semi-local likelihood regression estimator of the proportion based on group testing data. Journal of Nonparametric Statistics, 25(1), 209-221.
Worlund, D. D., & Taylor, G. (1983). Estimation of disease incidence in fish populations. Canadian Journal of Fisheries and Aquatic Sciences, 40(12), 2194-2197.
Xie, M. (2001). Regression analysis of group testing samples. Statistics in Medicine, 20(13), 1957-1969.
Yuan, A., Piao, J., Ning, J., & Qin, J. (2021). Semiparametric isotonic regression modelling and estimation for group testing data. Canadian Journal of Statistics, 49(3), 659-677.
Zhang, B., Bilder, C. R., & Tebbs, J. M. (2013). Group testing regression model estimation when case identification is a goal. Biometrical Journal, 55(2), 173-189.