Published

2017-07-01

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

DOI:

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

Keywords:

Cancer, lead time, sensitivity, sojourn time, transition density (en)
búsqueda de cáncer, densidad de transición, sensibilidad, tiempo de estadía, tiempo de ventaja (es)

Downloads

Authors

  • Ruiqi Liu University of Louisville
  • Jeremy Thomas Gaskins University of Louisville
  • Ritendranath Mitra University of Louisville
  • Dongfeng Wu University of Louisville

Early detection combined with effective treatments 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.

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.

References

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.

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.

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.

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.

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.

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).

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.

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).

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.

Prorok, P. C. (1976), ‘The Theory of Periodic Screening I: Lead Time and Proportion Detected’, Advances in Applied Probability 8(1), 127–143.

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.

Shen, Y., Wu, D. & Zelen, M. (2001), ‘Testing the Independence of Two Diagnostic Tests’, Biometrics 57(4), 1009–1017.

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.

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.

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.

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

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.

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.

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.

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.

Wu, D., Erwin, D. & Rosner, G. L. (2011), ‘Sojourn Time and Lead Time Projection in Lung Cancer Screening’, Lung Cancer 72(3), 322– 326.

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).

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.

Wu, D., Rosner, G. L. & Broemeling, L. D. (2007), ‘Bayesian Inference for the Lead Time in Periodic Cancer Screening’, Biometrics 63(3), 873–880.

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.

Zelen, M. & Feinleib, M. (1969), ‘On the Theory of Screening for Chronic Diseases’, Biometrika 56(3), 601–614.

How to Cite

APA

Liu, R., Gaskins, J. T., Mitra, R. and Wu, D. (2017). A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Revista Colombiana de Estadística, 40(2), 263–278. https://doi.org/10.15446/rce.v40n2.60642

ACM

[1]
Liu, R., Gaskins, J.T., Mitra, R. and Wu, D. 2017. A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Revista Colombiana de Estadística. 40, 2 (Jul. 2017), 263–278. DOI:https://doi.org/10.15446/rce.v40n2.60642.

ACS

(1)
Liu, R.; Gaskins, J. T.; Mitra, R.; Wu, D. A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Rev. colomb. estad. 2017, 40, 263-278.

ABNT

LIU, R.; GASKINS, J. T.; MITRA, R.; WU, D. A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Revista Colombiana de Estadística, [S. l.], v. 40, n. 2, p. 263–278, 2017. DOI: 10.15446/rce.v40n2.60642. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/60642. Acesso em: 20 apr. 2024.

Chicago

Liu, Ruiqi, Jeremy Thomas Gaskins, Ritendranath Mitra, and Dongfeng Wu. 2017. “A Review of Estimation of Key Parameters and Lead Time in Cancer Screening”. Revista Colombiana De Estadística 40 (2):263-78. https://doi.org/10.15446/rce.v40n2.60642.

Harvard

Liu, R., Gaskins, J. T., Mitra, R. and Wu, D. (2017) “A Review of Estimation of Key Parameters and Lead Time in Cancer Screening”, Revista Colombiana de Estadística, 40(2), pp. 263–278. doi: 10.15446/rce.v40n2.60642.

IEEE

[1]
R. Liu, J. T. Gaskins, R. Mitra, and D. Wu, “A Review of Estimation of Key Parameters and Lead Time in Cancer Screening”, Rev. colomb. estad., vol. 40, no. 2, pp. 263–278, Jul. 2017.

MLA

Liu, R., J. T. Gaskins, R. Mitra, and D. Wu. “A Review of Estimation of Key Parameters and Lead Time in Cancer Screening”. Revista Colombiana de Estadística, vol. 40, no. 2, July 2017, pp. 263-78, doi:10.15446/rce.v40n2.60642.

Turabian

Liu, Ruiqi, Jeremy Thomas Gaskins, Ritendranath Mitra, and Dongfeng Wu. “A Review of Estimation of Key Parameters and Lead Time in Cancer Screening”. Revista Colombiana de Estadística 40, no. 2 (July 1, 2017): 263–278. Accessed April 20, 2024. https://revistas.unal.edu.co/index.php/estad/article/view/60642.

Vancouver

1.
Liu R, Gaskins JT, Mitra R, Wu D. A Review of Estimation of Key Parameters and Lead Time in Cancer Screening. Rev. colomb. estad. [Internet]. 2017 Jul. 1 [cited 2024 Apr. 20];40(2):263-78. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/60642

Download Citation

CrossRef Cited-by

CrossRef citations1

1. Ruiqi Liu, Adriana Pérez, Dongfeng Wu. (2018). Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial. Journal of Healthcare Informatics Research, 2(4), p.353. https://doi.org/10.1007/s41666-018-0027-8.

Dimensions

PlumX

Article abstract page views

471

Downloads

Download data is not yet available.