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The Lead Time Distribution When Lifetime is Subject to Competing Risks in Cancer Screening

Author

Listed:
  • Wu Dongfeng

    (University of Louisville)

  • Kafadar Karen

    (Indiana University, Bloomington)

  • Rosner Gary L.

    (Sidney Kimmel Comprehensive Cancer Center)

  • Broemeling Lyle D.

    (Broemeling and Associates Inc.)

Abstract

This paper extends the previous probability model for the distribution of lead time in periodic cancer screening exams, namely, in that the lifetime T is treated as a random variable, instead of a fixed value. Hence the number of screens for a given individual is a random variable as well. We use the actuarial life table from the Social Security Administration to obtain the lifetime distribution, and then use this information to project the lead time distribution for someone with a future screening schedule. Simulation studies using the HIP study group data provide estimates of the lead time under different screening frequencies. The projected lead time has two components: a point mass at zero (corresponding to interval cases detected between screening exams) and a continuous probability density. We present estimates of the projected lead time for participants in a breast cancer screening program. The model is more realistic and can inform optimal screening frequency. This study focuses on breast cancer screening, but is applicable to other kinds of cancer screening also.

Suggested Citation

  • Wu Dongfeng & Kafadar Karen & Rosner Gary L. & Broemeling Lyle D., 2012. "The Lead Time Distribution When Lifetime is Subject to Competing Risks in Cancer Screening," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-16, April.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:6
    DOI: 10.1515/1557-4679.1363
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    References listed on IDEAS

    as
    1. Dongfeng Wu & Gary L. Rosner & Lyle Broemeling, 2005. "MLE and Bayesian Inference of Age-Dependent Sensitivity and Transition Probability in Periodic Screening," Biometrics, The International Biometric Society, vol. 61(4), pages 1056-1063, December.
    2. Kafadar, Karen & Prorok, Philip C., 1996. "Computer simulation of randomized cancer screening trials to compare methods of estimating lead time and benefit time," Computational Statistics & Data Analysis, Elsevier, vol. 23(2), pages 263-291, December.
    3. Dongfeng Wu & Gary L. Rosner & Lyle D. Broemeling, 2007. "Bayesian Inference for the Lead Time in Periodic Cancer Screening," Biometrics, The International Biometric Society, vol. 63(3), pages 873-880, September.
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    Cited by:

    1. John D. Rice & Brent A. Johnson & Robert L. Strawderman, 2022. "Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 605-636, October.

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