Bayesian sample-size determination for inference on two binomial populations with no gold standard classifier

Statistics in Medicine

Volumn 24, Issue 19, 2005, Pages 2963-2976

  Stamey, James D ^a   Seaman, John W ^b   Young, Dean M ^b  

^a STEPHEN F AUSTIN STATE UNIVERSITY   (United States)

^b BAYLOR UNIVERSITY   (United States)

Author keywords

Average coverage criterion; Average length criterion; Misclassification; Normal approximation

Indexed keywords

ARTICLE; BAYES THEOREM; BIOSTATISTICS; DATA ANALYSIS; DIAGNOSTIC TEST; MATHEMATICAL COMPUTING; POPULATION RESEARCH; SAMPLE SIZE; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS; STRONGYLOIDIASIS;

ANIMALS; BAYES THEOREM; CAMBODIA; COMPUTER SIMULATION; DIAGNOSTIC TESTS, ROUTINE; HUMANS; MICROSCOPY; MODELS, STATISTICAL; MONTE CARLO METHOD; REFUGEES; RESEARCH DESIGN; SAMPLE SIZE; SEROLOGY; STRONGYLOIDES; STRONGYLOIDIASIS;

EID: 26444560165     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.2167     Document Type: Article

References (29)

1. Pan W. Approximate confidence intervals for one proportion and difference of two proportions. Computational Statistics and Data Analysis 2002; 40(1):143-157.
2. Feigin PD, Lumelskii YP. On confidence limits for the difference of two binomial parameters. Communications in Statistics-Theory and Methods 2000; 29(1):131-141.
3. Railkar RA, Mehrotra DV, Iglewicz B. Simultaneous testing strategy for comparing two treatments in a stratified binomial trial. Journal of Biopharmaceutical Statistics 2000; 10(3):335-349.
4. Agresti A, Caffo B. Simple and effective confidence intervals for proportions and differences of proportions resulting from adding two successes and two failures. The American Statistician 2000; 54(4):280-288.
5. Cai H. A normal approximation theorem in comparing two binomial distributions. Statistics and Probability Letters 2000; 48(1):83-89.
6. Joseph L, du Berger R, Bélisle P. Bayesian and mixed Bayesian/likelihood criteria for sample size determination. Statistics in Medicine 1997; 16(7):769-781.
7. Pham-Gia T, Turkkan N. Determination of the exact sample sizes in the Bayesian analysis of the difference of two proportions. Journal of the Royal Statistical Society Series D 2003; 52:131-150.
8. Katsis A, Toman B. Bayesian sample size calculations for binomial experiments. Journal of Statistical Planning and Inference 1999; 81(2):349-362.
9. Tenenbein A. A double sampling scheme for estimating from binomial data with misclassification. Journal of the American Statistical Association 1970; 65(331):1350-1361.
10. Hochberg Y. On the use of double sampling schemes in analyzing categorical data with misclassification errors. Journal of the American Statistical Association 1977; 72(360):914-921.
11. Chen TT. Log-linear models for categorical data with misclassification and double sampling. Journal of the American Statistical Association 1979; 74(366):481-488.
12. Viana M, Ramakrishnan V, Levy P. Bayesian analysis of prevalence from the results of small screening samples. Communications in Statistics-Theory and Methods 1993; 22(2):575-585.
13. Joseph L, Gyorkos TW, Coupal L. Bayesian estimation of disease prevalence and parameters of diagnostic tests in the absence of a gold standard. American Journal of Epidemiology 1995; 141(3):263-272.
14. Gustafson P, Le ND, Saskin R. Case-control analysis with partial knowledge of exposure misclassification probabilities. Biometrics 2001; 57(2):598-609.
15. Chernoff H, Haitovsky Y. Locally optimal design for comparing two probabilities from binomial data subject to misclassification. Biometrika 1990; 77(4):797-805.
16. Palmgren J. Precision of double sampling estimators for comparing two probabilities. Biometrika 1987; 74(4):687-694.
17. 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.
18. Dendukuri N, Rahme E, Bélisle P, Joseph L. Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test. Biometrics 2004; 60:388-397.
19. Zou KH, Normand S-LT. On determination of sample size in hierarchical binomial models. Statistics in Medicine 2001; 20(14):2163-2182.
20. Stamey JD, Seaman JW Jr, Young DM. Bayesian sample-size determination for estimating a Poisson rate with underreported data. Communications in Statistics-Simulation and Computation 2004; 33(2):341-354.
21. Adcock CJ. Sample size determination: a review. Statistician 1997; 46(2):261-283.
22. Prescott GJ, Garthwaite PH. A simple Bayesian analysis of misclassified binary data with a validation sub-study. Biometrics 2002; 58(2):454-458.
23. Gustafson P. On model expansion, model contraction, identifiability, and prior information: two illustrative scenarios involving mismeasured variables. Statistical Science, to appear.
24. Singer RS, Boyce WM, Gardner IA, Johnson WO, Fisher AS. Evaluation of bluetongue virus diagnostic tests in free-ranging bighorn sheep. Preventative Veterinary Medicine 1998; 35(4):265-282.
25. Meng X-L, Rubin DB. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association 1991; 86(416):899-909.
26. Ferguson TS. A Course in Large Sample Theory. Chapman & Hall: London, 1996.
27. Muller P, Pargimianni G. Optimal design via curve fitting of Monte Carlo experiments. Journal of the American Statistical Association 1995; 90:1322-1330.
28. Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 2001; 57(1):208-217.
29. Georgiadis MP, Johnson WO, Gardner IA, Singh R. Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests. Applied Statistics 2003; 52:63-76.