Global hypothesis test to compare the likelihood ratios of multiple binary diagnostic tests with ignorable missing data

  1. Marín Jiménez, Ana Eugenia
  2. Roldán Nofuentes, José A.
Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Año de publicación: 2014

Volumen: 38

Número: 2

Páginas: 305-324

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Sort: Statistics and Operations Research Transactions

Resumen

In this article, a global hypothesis test is studied to simultaneously compare the likelihood ratios of multiple binary diagnostic tests when in the presence of partial disease verification the missing data mechanism is ignorable. The hypothesis test is based on the chi-squared distribution. Simulation experiments were carried out to study the type I error and the power of the global hypothesis test when comparing the likelihood ratios of two and three diagnostic tests respectively. The results obtained were applied to the diagnosis of coronary stenosis.

Referencias bibliográficas

  • Agresti, A. (2002). Categorical Data Analysis. New York: Wiley.
  • Begg, C. B. and Greenes, R. A. (1983). Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics, 39, 207–215.
  • Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.
  • Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biométrica, 75, 800–802.
  • Holm, S. (1979). A simple sequential rejective multiple testing procedure. Scandinavian. Journal of Statistics, 6, 65–70.
  • Leisenring, W. and Pepe, M. S. (1998). Regression modelling of diagnostic likelihood ratios for the evaluation of medical diagnostic tests. Biometrics, 54, 444–452.
  • Luts, J., Roldán Nofuentes, J. A., Luna del Castillo, J. D. and Van Huffel, S. (2011). Asymptotic hypothesis test to compare likelihood ratios of multiple diagnostic tests in unpaired designs. Journal of Statistical Planning and Inference, 141, 3578–3594.
  • Roldán Nofuentes, J. A. and Luna del Castillo, J. D. (2005). Comparing the likelihood ratios of two binary diagnostic tests in the presence of partial verification. Biometrical Journal, 47, 442–457.
  • Roldán Nofuentes, J. A. and Luna del Castillo, J. D. (2007). Comparison of the likelihood ratios of two binary diagnostic tests in paired designs. Statistics in Medicine, 26, 4179–4201.
  • Rubin, D. B. (1976). Inference and missing data. Biometrika, 4, 73–89.
  • Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. USA: Chapman and Hall/CRC.
  • Torrance-Rynard, V. L. and Walter, S. D. (1997). Effects of dependent errors in the assessment of diagnostic test performance. Statistics in Medicine, 16, 2157–2175.
  • Vacek, P. (1985). The effect of conditional dependence on the evaluation of diagnostic tests. Biometrics, 41, 959–968.
  • Zhou, X. H. (1993). Maximum likelihood estimators of sensitivity and specificity corrected for verification bias. Communication in Statistics-Theory and Methods, 22, 3177–3198.
Fuente de los datos: Dialnet