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

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.

[1]  P. Prorok,et al.  Non-parametric estimation of the post-lead-time survival distribution of screen-detected cancer cases. , 1995, Statistics in medicine.

[2]  K. Kafadar,et al.  A data-analytic approach for estimating lead time and screening benefit based on survival curves in randomized cancer screening trials. , 1994, Statistics in medicine.

[3]  A. Miller,et al.  Periodic Screening for Breast Cancer: The Health Insurance Plan Project and its Sequelae, 1963–1986 , 1989 .

[4]  K. Kafadar,et al.  Alternative definitions of comparable case groups and estimates of lead time and benefit time in randomized cancer screening trials , 2003, Statistics in medicine.

[5]  Dongfeng Wu,et al.  MLE and Bayesian Inference of Age‐Dependent Sensitivity and Transition Probability in Periodic Screening , 2005, Biometrics.

[6]  Marvin Zelen,et al.  Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms , 2009 .

[7]  L. Broemeling,et al.  Bayesian Inference for the Lead Time in Periodic Cancer Screening , 2007, Biometrics.

[8]  P C Prorok,et al.  Estimation of post-lead-time survival under dependence between lead-time and post-lead-time survival. , 1999, Statistics in medicine.

[9]  Philip C. Prorok IN THE DESIGN OF A REPETITIVE SCREENING PROGRAM , 1982 .

[10]  H Straatman,et al.  Estimating lead time and sensitivity in a screening program without estimating the incidence in the screened group. , 1997, Biometrics.

[11]  Philip C. Prorok,et al.  Computer simulation of randomized cancer screening trials to compare methods of estimating lead time and benefit time , 1996 .

[12]  K. Kafadar,et al.  An Estimate of the Variance of Estimators for Lead Time and Screening Benefit in Randomised Cancer Screening Trials , 1996 .

[13]  S D Walter,et al.  Estimation of the duration of a pre-clinical disease state using screening data. , 1983, American journal of epidemiology.

[14]  Marvin Zelen,et al.  On the theory of screening for chronic diseases , 1969 .