SciELO - Scientific Electronic Library Online

 
vol.40 issue2Monitoring Aggregated Poisson Data for Processes with Time-Varying Sample SizesGoodness of Fit Tests for Rayleigh Distribution Based on Phi-Divergence author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Revista Colombiana de Estadística

Print version ISSN 0120-1751

Rev.Colomb.Estad. vol.40 no.2 Bogotá July/Dec. 2017

https://doi.org/10.15446/rce.v40n2.60642 

http://dx.doi.org/10.15446/rce.v40n2.60642

A Review of Estimation of Key Parameters and Lead Time in Cancer Screening

Una revisión de la estimación de los parámetros claves y el tiempo de ventaja en la búsqueda de cáncer

RUIQI LIU1, JEREMY T. GASKINS2, RITENDRANATH MITRA3, DONGFENG WU4

1University of Louisville, School of Public Health and Information Sciences, Department of Bioinformatics and Biostatistics, Louisville, United States. PhD. Email: ruiqi.liu@louisville.edu
2University of Louisville, School of Public Health and Information Sciences, Department of Bioinformatics and Biostatistics, Louisville, United States. PhD. Email: jeremy.gaskins@louisville.edu
3University of Louisville, School of Public Health and Information Sciences, Department of Bioinformatics and Biostatistics, Louisville, United States. PhD. Email: ritendranath.mitra@louisville.edu
4University of Louisville, School of Public Health and Information Sciences, Department of Bioinformatics and Biostatistics, Louisville, United States. PhD. Email: dongfeng.wu@louisville.edu


Abstract

Early detection combined with effective treatment are the only ways to fight against cancer, and cancer screening is the primary technique for early detection. Although mass cancer screening has been carried out for decades, there are many unsolved problems, and the statistical theory of cancer screening is still under developed. Screening sensitivity, time duration in the preclinical state, and time duration in the disease free state are the three key parameters, which are critical in cancer screening, since all other estimates are functions of the three key parameters. Lead time is the diagnosis time advanced by screening, and it serves as a measurement of effectiveness of screening programs. In this article, we provide a review for major probability models and statistical methodologies that have been developed on the estimation of the three key parameters and the lead time distributions. These methods can be applied to screening of other chronic diseases after slight modifications.

Key words: Cancer, Lead time, Sensitivity, Sojourn time, Transition\linebreak density.


Resumen

Detección temprana combinada con la efectividad de los tratamientos son las únicas formas de combatir en contra del cáncer, y el examen de búsqueda temprana es la técnica principal para detección temprana. A pesar de que la búsqueda temprana de la masa cancerígena se ha realizado pro décadas, hay muchos problemas sin resolver, y la teoría estadística de la búsqueda del cáncer está todavía en desarrollo. Los tres parámetros claves: sensibilidad de la búsqueda, la duración en tiempo en el estado pre-clínico, y la duración en tiempo de la enfermedad en estado libre, son críticos en la búsqueda de cáncer; esto es porque todos los otros estimadores son funciones de estos tres parámetros claves. El tiempo de ventaja es el tiempo de diagnóstico avanzado por la búsqueda, y sirve como una medida de la efectividad de los programas de búsqueda. En este artículo, presentamos una revisión de los modelos de probabilidad principales y las metodologías estadísticas que han sido desarrolladas en la estimación de los tres parámetros claves y las distribuciones del tiempo de ventaja. Estos métodos pueden ser aplicados a la búsqueda de otras enfermedades crónicas con modificaciones menores.

Palabras clave: búsqueda de cáncer, densidad de transición, sensibilidad, tiempo de estadía, tiempo de ventaja.


Texto completo disponible en PDF


References

1. Chen, Y., Brock, G. & Wu, D. (2010), 'Estimating Key Parameters in Periodic Breast Cancer Screening-Application to the Canadian National Breast Screening Study Data', Cancer Epidemiology 34(4), 429-433.         [ Links ]

2. Jang, H., Kim, S. & Wu, D. (2013), 'Bayesian Lead Time Estimation for the Johns Hopkins Lung Project Data', Journal of Epidemiology and Global Health 3(3), 157-163.         [ Links ]

3. Kafadar, K. & Prorok, P. C. (1994), 'A Data-Analytic Approach for Estimating Lead Time and Screening Benefit Based on Survival Curves in Randomized Cancer Screening Trials', Statistics in Medicine 13(5-7), 569-586.         [ Links ]

4. Kafadar, K. & Prorok, P. C. (1996), 'Computer Simulation of Randomized Cancer Screening Trials to Compare Methods of Estimating Lead Time and Benefit Time', Computational Statistics and Data Analysis 23(2), 263 - 291.         [ Links ]

5. Kafadar, K. & Prorok, P. C. (2003), 'Alternative Definitions of Comparable Case Groups and Estimates of Lead Time and Benefit Time in Randomized Cancer Screening Trials', Statistics in Medicine 22(1), 83-111.         [ Links ]

6. Kendrick, S. K., Rai, S. N. & Wu, D. (2015), 'Simulation Study for the Sensitivity and Mean Sojourn Time Specific Lead Time in Cancer Screening When Human Lifetime is a Competing Risk', Journal of Biometrics and Biostatistics 6(4).         [ Links ]

7. Kim, S. & Wu, D. (2016), 'Estimation of Sensitivity Depending on Sojourn Time and Time Spent in Preclinical State', Statistical Methods in Medical Research 25(2), 728-740.         [ Links ]

8. Liu, R., Levitt, B., Riley, T. & Wu, D. (2015), 'Bayesian Estimation of the Three Key Parameters in CT for the National Lung Screening Trial Data', Journal of Biometrics and Biostatistics 6(5).         [ Links ]

9. Prevost, T. C., Launoy, G., Duffy, S. W. & Chen, H. H. (1998), 'Estimating Sensitivity and Sojourn Time in Screening for Colorectal Cancer: A Comparison of Statistical Approaches', American Journal of Epidemiology 148(6), 609-619.         [ Links ]

10. Prorok, P. C. (1976), 'The Theory of Periodic Screening I: Lead Time and Proportion Detected', Advances in Applied Probability 8(1), 127-143.         [ Links ]

11. Prorok, P. C. (1982), 'Bounded Recurrence Times and Lead Time in the Design of a Repetitive Screening Program', Journal of Applied Probability 19(1), 10-19.         [ Links ]

12. Shen, Y., Wu, D. & Zelen, M. (2001), 'Testing the Independence of Two Diagnostic Tests', Biometrics 57(4), 1009-1017.         [ Links ]

13. Shen, Y. & Zelen, M. (1999), 'Parametric Estimation Procedures for Screening Programmes: Stable and Nonstable Disease Models for Multimodality Case Finding', Biometrika 86(3), 503-515.         [ Links ]

14. Shows, J. & Wu, D. (2011), 'Inferences for the Lead Time in Breast Cancer Screening Trials under a Stable Disease Model', Cancers 3(2), 2131 - 2140.         [ Links ]

15. Straatman, H., Peer, P. G. M. & Verbeek, A. L. M. (1997), 'Estimating Lead Time and Sensitivity in a Screening Program without Estimating the Incidence in the Screened Group', Biometrics 53(1), 217-229.         [ Links ]

16. USPSTF, (2016), The United States Preventive Services Task Force. *https://www.uspreventiveservicestaskforce.org         [ Links ]

17. Walter, S. D. & Day, N. E. (1983), 'Estimation of the Duration of A Preclinical Disease State Using Screening Data', American Journal of Epidemiology 118(6), 865-886.         [ Links ]

18. Wu, D., Cariño, R. L. & Wu, X. (2008), 'When Sensitivity is a Function of Age and Time Spent in the Preclinical State in Periodic Cancer Screening', Journal of Modern Applied Statistical Methods 7(1), 297-303.         [ Links ]

19. Wu, D., Erwin, D. & Rosner, G. L. (2009a), 'A Projection of Benefits Due to Fecal Occult Blood Test for Colorectal Cancer', Cancer Epidemiology 33(3), 212-215.         [ Links ]

20. Wu, D., Erwin, D. & Rosner, G. L. (2009b), 'Estimating Key Parameters in FOBT Screening for Colorectal Cancer', Cancer Causes and Control 20(1), 41-46.         [ Links ]

21. Wu, D., Erwin, D. & Rosner, G. L. (2011), 'Sojourn Time and Lead Time Projection in Lung Cancer Screening', Lung Cancer 72(3), 322- 326.         [ Links ]

22. Wu, D., Kafadar, K., Rosner, G. L. & Broemeling, L. D. (2012), 'The Lead Time Distribution When Lifetime is Subject to Competing Risks in Cancer Screening', The International Journal of Biostatistics 8(1).         [ Links ]

23. Wu, D., Rosner, G. L. & Broemeling, L. D. (2007), 'Bayesian Inference for the Lead Time in Periodic Cancer Screening', Biometrics 63(3), 873-880.         [ Links ]

24. Wu, D., Rosner, G. L. & Broemeling, L. (2005), 'MLE and Bayesian Inference of Age-Dependent Sensitivity and Transition Probability in Periodic Screening', Biometrics 61(4), 1056-1063.         [ Links ]

25. Wu, D., Wu, X., Banicescu, I. & Cariño, R. L. (2005), 'Simulation Procedure in Periodic Cancer Screening Trials', Journal of Modern Applied Statistical Methods 4(2), 522-527.         [ Links ]

26. Zelen, M. & Feinleib, M. (1969), 'On the Theory of Screening for Chronic Diseases', Biometrika 56(3), 601-614.         [ Links ]


[Recibido en octubre de 2016. Aceptado en abril de 2017]

Este artículo se puede citar en LaTeX utilizando la siguiente referencia bibliográfica de BibTeX:

@ARTICLE{RCEv40n2a04,
    AUTHOR  = {Liu, Ruiqi and Gaskins, Jeremy T. and Mitra, Ritendranath and Wu, Dongfeng},
    TITLE   = {{A Review of Estimation of Key Parameters and Lead Time in Cancer Screening}},
    JOURNAL = {Revista Colombiana de Estadística},
    YEAR    = {2017},
    volume  = {40},
    number  = {2},
    pages   = {263-278}
}

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License