Modeling the overdetection of screen‐identified cancers in population‐based cancer screening with the Coxian phase‐type Markov process

Abstract

Modeling overdetection resulting from screening often uses the conventional competing risk model. This model assigns screen-detected cases dying from other causes as overdetection modeled by a one-jump process, which may not be true for the censored overdetected cases. To relax this restrictive assumption, accommodate a finite Markov process for overdetection, and dispense with long-term follow-up until death, we propose a generalized Coxian phase-type Markov process to distinguish the progressive latent multistate pathway from the nonprogressive (overdetected) latent multistate pathway. Various new likelihood functions were developed to estimate the transition parameters with the available data accrued at the time of diagnosis. The proportion of overdetected cancers by the cured model was further estimated by using parameters with and without distinguishing between the two latent pathways. While perturbation analyses were conducted by changing their parameters to assess their effects on overdetection, the results, including of asymptotic analyses, were very robust for an overdetection rate higher than 20% but not for low overdetection rates. These two scenarios were demonstrated by applying the Coxian phase-type model to prostate cancer and breast cancer screening, yielding a substantial proportion of overdetected prostate cancer (60%) attributed to the prostate specific antigen test and a small fraction of overdetected breast cancer (3%) detected by mammography. This kind of variation in overdetection elucidated by the Coxian phase-type Markov process provides new insights into the quantitative mechanisms producing overdetection, which is informative for evaluating the benefits and risks of various types of population-based cancer screening programs.

Assessing overdiagnosis of fecal immunological test screening for colorectal cancer with a digital twin approach

“…In contrast to 20% to 60% overdiagnosis [26][27][28] done to the population-based FIT service screening program is as minor seen with gFOBT. Furthermore, when only invasive cancers were included, the estimated proportion of overdiagnosis based on FIT population-based screening decreased from 9.90% to 4.16%.…”

“…In contrast to 20% to 60% overdiagnosis 26 – 28 observed in PSA screening for prostate cancer, the proportion of overdiagnosis for FIT population-based screening is modest, implying that the harm done to the population-based FIT service screening program is as minor seen with gFOBT. Furthermore, when only invasive cancers were included, the estimated proportion of overdiagnosis based on FIT population-based screening decreased from 9.90% to 4.16%.…”

A pre-symptomatic incubation model for precision strategies of screening, quarantine, and isolation based on imported COVID-19 cases in Taiwan

“…S2 . The mathematical likelihood function forms refer to the previous study on the application of the stochastic model 35 – 37 . Note that the asymptomatic cases detected by RT-PCR test would be analogous to over-detected cases if the screening is offered as asymptomatic COVID-19 like over-detected cases in screening would have not been found had the RT-PCR screening been not administered.…”

Sojourn-time-corrected receiver operating characteristic curve (ROC) for prostate specific antigen (PSA) test in population-based prostate cancer screening

“…To cope with the incomplete ascertainment of PrCa below the pre-determined cut-off of the PSA test in population-based PrCa screening, the PSA-negative cohort requires a follow-up time. The false negative cases are dependent on the length of follow-up time and associated with MST, which may also be expressed as the average duration that the cancer stays in the preclinical detectable phase [5][6][7] . The cancers detected at screening, which are generally called screen-detected cancer, were occult cancers in the preclinical detectable phase.…”

“…However, this approach is not theoretically sound because the follow-up time for allowing these missed cancers to surface as clinical cases is dependent on the length of the follow-up time required for a valid estimation of risk after a negative screening test, which is subject to the rate of disease progression from a pre-clinical detectable phase (PCDP, cancer in an asymptomatic phase that may be detected by a test) to the clinical phase (CP, cancer in a symptomatic phase), namely mean sojourn time (MST), which is the average duration of staying in PCDP 5,6 . The estimates of MST for PrCa is rather long at approximately 6.4-6.75 years 6,7 . This duration means that false negative cases may be incompletely ascertained if the follow-up time is not sufficient compared based on the long MST for PrCa.…”