Bayesian sample size for diagnostic test studies in the absence of a gold standard: Comparing identifiable with non-identifiable models

Statistics in Medicine

Volumn 29, Issue 26, 2010, Pages 2688-2697

  Dendukuri, Nandini ^a,b   Bélisle, Patrick ^b   Joseph, Lawrence ^a,b  

^a MCGILL UNIVERSITY   (Canada)

^b ROYAL VICTORIA HOSPITAL   (Canada)

Author keywords

Bayesian design; Diagnostic test; Latent class model; Misclassification; Sample size

Indexed keywords

ARTICLE; BAYES THEOREM; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST; DISEASE TRANSMISSION; GOLD STANDARD; PREVALENCE; SAMPLE SIZE; SENSITIVITY ANALYSIS; STRONGYLOIDIASIS; TUBERCULOSIS;

ANIMALS; BAYES THEOREM; DIAGNOSTIC TECHNIQUES AND PROCEDURES; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; RESEARCH DESIGN; SAMPLE SIZE; STRONGYLOIDES; STRONGYLOIDIASIS; TUBERCULOSIS;

EID: 77958564508     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4037     Document Type: Article

References (42)

1. Walter S, Irwig L. Estimation of error rates, disease prevalence and relative risks from misclassified data: a review. Journal of Clinical Epidemiology 1988; 41:923-937.
2. Espeland M, Handelman S. Using latent class models to characterize and assess relative error in discrete measurements. Biometrics 1989; 45:587-599.
3. Joseph L, Gyorkos T, Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. American Journal of Epidemiology 1995; 141:263-272.
4. Demissie K, White N, Joseph L, Ernst P. Bayesian estimation of asthma prevalence, and comparison of exercise and questionnaire diagnostics in the absence of a gold standard. Annals of Epidemiology 1998; 8:201-208.
5. Johnson W, Gastwirth J, Pearson L. Screening without a 'gold standard': the Hui-Walter paradigm revisited. American Journal of Epidemiology 2001; 153:921-924.
6. Qu Y, Tan M, Kutner M. Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 1996; 52:797-810.
7. Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 2000; 57:208-217.
8. Black M, Craig B. Estimating disease prevalence in the absence of a gold standard. Statistics in Medicine 2002; 21:2653-2669.
9. Branscum S, Gardner I, Johnson W. Estimation of diagnostic-test sensitivity and specificity through bayesian modeling. Preventive Veterinary Medicine 2005; 68:145-163.
10. Menten J, Boelaert M, Lesaffre E. Bayesian latent class models with conditionally dependent diagnostic tests: a case study. Statistics in Medicine 2008; 27:4469-4488.
11. Dendukuri N, Hadgu A, Wang L. Modeling conditional dependence between diagnostic tests: a multiple latent variable model. Statistics in Medicine 2009; 28:441-461.
12. Weber MF, Verhoeff J, van Schaik G, van Maanen C. Evaluation of Ziehl-Neelsen stained faecal smear and ELISA as tools for surveillance of clinical paratuberculosis in cattle in the netherlands. Preventive Veterinary Medicine 2009; 92:256-266.
13. Monti G, Frankena K, Engel B, Buist W, Tarabla H, de Jong M. Evaluation of a new antibody-based enzyme-linked immunosorbent assay for the detection of bovine leukemia virus infection in dairy cattle. Journal of Veterinary Diagnostic Investigation 2005; 17:451-457.
14. Kass R, Raftery A. Bayes factors. Journal of the American Statistical Association 2005; 90:773-795.
15. Bernatsky S, Joseph L, Bélisle P, Boivin J, Rajan R, Moore A, Clarke A. A Bayesian hierarchical model for estimating the properties of cancer ascertainment methods in cohort studies. Statistics in Medicine 2005; 24:2365-2379.
16. Carabin H, Marshall C, Joseph L, Riley S, Olveda R, McGarvey S. Estimating and modelling the dynamics of the intensity of infection with Schistosoma japonicum in villagers of Leyte, Philippines. Part I: a Bayesian cumulative logit model. American Journal of Tropical Medicine and Hygiene 2005; 72:745-753.
17. Moayyedi P, Duffy J, Delaney B. New approaches to enhance the accuracy of the diagnosis of reflux disease. Gut 2004; 53:iv55-iv57.
18. Scott A, Joseph L, Bélisle P, Behr M, Schwartzman K. Bayesian estimation of tuberculosis clustering rates from DNA sequence data. Statistics in Medicine 2008; 27:140-156.
19. Weichenthal S, Joseph L, Bélisle P, Dufresne A. Bayesian estimation of the probability of asbestos exposure from lung fibre counts. Biometrics 2010; 66(2):603-612. DOI: 10.1111/j.1541-0420.2009.01279.x.
20. Tarafder M, Carabin H, Joseph L, Balolong E, Olveda R, McGarvey S. Estimating the sensitivity and specificity of Kato-Katz stool examination technique for detection of hookworms, Ascaris lumbricoides, and Trichuris trichiura infections in humans in the absence of a gold standard. International Journal for Parasitology 2010; 40:399-404.
21. Ladouceur M, Rahme E, Pineau C, Joseph L. Robustness of prevalence estimates derived from misclassified data from administrative databases. Biometrics 2007; 63:272-279.
22. Rose N, Boutrouille A, Fablet C, Madec F, Eloit M, Pavio N. The use of Bayesian methods for evaluating the performance of a virus-like particles-based ELISA for serology of Hepatitis E virus infection in swine. Journal of Virological Methods 2010; 163:329-335.
23. Benitoa A, Carmena D, Joseph L, Martineza J, Guisantesa J. Dog echinococcosis in northern Spain: comparison of coproantigen and serum antibody assays with coprological exam. Veterinary Parasitology 2006; 142:102-111.
24. Engel B, Buist W, Orsel K, Dekker A, de Clercq K, Grazioli S, van Roermund H. A Bayesian evaluation of six diagnostic tests for foot-and-mouth disease for vaccinated and non-vaccinated cattle. Preventive Veterinary Medicine 2008; 86:124-138.
25. Nérette P, Stryhn H, Dohoo I, Hammell L. Using pseudogold standards and latent-class analysis in combination to evaluate the accuracy of three diagnostic tests. Preventive Veterinary Medicine 2008; 85:207-225.
26. Boelaert M, El-Safi S, Hailu A, Mukhtar M, Rijal S, Sundar S, Wasunna M, Aseffa A, Mbui J, Menten J, Desjeux P, Peeling RW. Diagnostic tests for kala-azar: a multi-centre study of the freeze-dried DAT, rK39 strip test and KAtex in East Africa and the Indian subcontinent. Transactions of the Royal Society of Tropical Medicine and Hygiene 2008; 102:32-40.
27. Kostoulas P, Leontides L, Ene C, Billinis C, Florou M, Sofia M. Bayesian estimation of sensitivity and specificity of serum ELISA and faecal culture for diagnosis of paratuberculosis in Greek dairy sheep and goats. Preventive Veterinary Medicine 2006; 76:56-73.
28. Alonzo T, Pepe M. Using a combination of reference tests to assess the accuracy of a new diagnostic test. Statistics in Medicine 1999; 18:2987-3003.
29. Pepe M. The Statistical Evaluation of Medical Tests For Classification and Prediction. Oxford University Press: Oxford, 2003.
30. Gustafson P. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Chapman & Hall: New York, 2003.
31. Broemelling L. Bayesian Biostatistics and Diagnostic Medicine. Wiley: New York, 2007.
32. Rahme E, Joseph L, Gyorkos T. Bayesian sample size determination for estimating binomial parameters from data subject to misclassification. Applied Statistics 2000; 49:119-128.
33. Dendukuri N, Rahme E, Bélisle P, Joseph L. Bayesian sample size determination for prevalence and diagnostic studies in the absence of a gold standard test. Biometrics 2004; 60:388-397.
34. Arkin CF, Wachtel MS. How many patients are necessary to assess test performance? JAMA 1990; 263:275-278.
35. Irwig L, Glasziou PP, Berry G, Chock C, Mock P, Simpson JM. Efficient study designs to assess the accuracy of screening tests. American Journal of Epidemiology 1994; 140:759-769.
36. Gustafson P. On model expansion, model contraction, identifiability, and prior information: two illustrative scenarios involving mismeasured variables (with Discussion). Statistical Science 2005; 20:111-140.
37. Adcock CJ. Sample size determination: a review. The Statistician 1997; 46:261-283.
38. Wang F, Gelfand AE. A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models. Statistical Science 2002; 17:193-208.
39. Joseph L, Bélisle P. Bayesian sample size determination for normal means and differences between normal means. The Statistician 1997; 46:209-226.
40. M'Lan C, Joseph L, Wolfson D. Bayesian sample size determination for case-control studies. Journal of the American Statistical Association 2006; 101:760-772.
41. Joseph L, du Berger R, Bélisle P. Bayesian and mixed Bayesian/likelihood criteria for sample size determination. Statistics in Medicine 1997; 16:769-781.
42. Desu M, Raghavarao D. Sample Size Methodology. Academic Press: Boston, 1990.