Queue hurdle Coxian phase-type model for two-stage process of population-based cancer screening

Abstract

The quality assurance of two-stage population-based cancer screening program is determined by arrival rate (attending screening), positive rate (determined by the criteria of screening test), the compliance and the waiting time (WT) for confirmatory diagnosis in those screened as positive. These parameters were correlated between the process of screening procedures and the effectiveness of screening program. To capture such an inter-dependence of these parameters and quantify the effectiveness of program, we proposed a Queue hurdle Coxian phase-type (QH-CPH) model to estimate the arrival rate of screenees with the Poisson Queue process and the compliance rate of confirmatory diagnosis with the hurdle model, and also to identify the hidden states of WT that is affected by the capacity of health care and relevant covariates (such as demographic features and geographic areas) with the Coxian phase-type (CPH) process. We applied the proposed QH-CPH model to Taiwanese nationwide colorectal cancer screening program data for estimating the arrival rate and the probability of not complying with colonoscopy and classifying the compliers into two hidden states, short-waiting phase and long-waiting phase for colonoscopy. Significant covariates responsible for three processes were also identified by using the proportional hazards regression forms. A simulation study was further performed to assess the joint effect of these parameters on WT through a series of scenarios. The proposed QH-CPH model can provide an insight into the optimal and the practical design on population-based cancer screening for health policy-makers given the limited health care resources and capacity.

Screening History and 7-Year Survival in 32,099 Colorectal Cancer Patients: A Population-Based Cohort Study

“…To address this bias, we referred to a study that evaluated transition rates of every colorectal cancer stage based on this Taiwanese population-based screening. 35 Using that transition rates, we adjusted for possible lead time in screen detection among FIT-positive patients with follow-up examinations. 21 After lead-time correction, there remains a significant difference in crude hazard rates between FIT-positive patients with a follow-up examination and never-screened patients (Model 3 in Supplementary Table S4 ); the time to diagnosis within 12 months among FIT-positive patients with a follow-up examination showed the lowest risk of colorectal cancer mortality ( Supplementary Table S11 ), and ever-screened patients exhibited a significant survival advantage over never-screened patients ( Supplementary Figure S2 ).…”