Abstract:Our calibration suggests that not all cancers are at risk of progression, and screening sensitivity may be lower at older ages. PCa screening models that use calibration to simulate disease progression in the unobservable latent phase are highly sensitive to prevalence assumptions.
“…1 and Table 1. Further details of the model, including its calibration and validation have been described earlier [41]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…to exit distant G < 7 cancer state (p / scale / shape) a
0.999 / 0.254 / 5.373calibrated [41] Prob. to exit distant G = 7 cancer state (p / scale / shape) a
0.945 / 0.806 / 4.564calibrated [41] Prob. to exit distant G > 7 cancer state (p / scale / shape) a
0.999 / 1.135 / 5.521calibrated [41] Familial risk factor on PCa onset and progression functions a
1.423calibrated [41] Prob.…”
Section: Methodsmentioning
confidence: 99%
“…However, out of the more than 25 PCa screening models [15–41], developed since the early 1990s, only five [21, 29, 37, 40, 41] account for both QoL and overdiagnosis with inconclusive results. While three of the five models [21, 29, 37] including the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model predict gains in quality-adjusted life expectancy (QALE) by screening up to age 70 and above in men with average PCa risk, two models [40, 41] including the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model predict potential losses. Estimates from the Erasmus MISCAN model suggest that overdiagnosis among screen-detected cancers might be as high as 50% [37, 42, 43].…”
Section: Introductionmentioning
confidence: 99%
“…Estimates from the Erasmus MISCAN model suggest that overdiagnosis among screen-detected cancers might be as high as 50% [37, 42, 43]. Estimates from the ONCOTYROL PCOP Model indicate even higher percentages [41]. …”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the model was recalibrated to match data from autopsy studies as well. Recalibration to higher prevalence, assuming that latent PCa detectable at autopsy might be detectable by screening as well, resulted in a considerable increase in overdiagnosis and decline in screening sensitivity, which shifted the benefit-harm balance of screening from QALE gains to losses [41]. However, previous analyses with the PCOP Model primarily focused on the effect of prevalence assumptions on the benefit-harm balance of screening and did not represent a comprehensive benefit-harm analysis.…”
BackgroundA recent recalibration of the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) Model, assuming that latent prostate cancer (PCa) detectable at autopsy might be detectable by screening as well, resulted in considerable worsening of the benefit-harm balance of screening. In this study, we used the recalibrated model to assess the effects of familial risk, quality of life (QoL) preferences, age, and active surveillance.MethodsMen with average and elevated familial PCa risk were simulated in separate models, differing in familial risk parameters. Familial risk was assumed to affect PCa onset and progression simultaneously in the base-case, and separately in scenario analyses. Evaluated screening strategies included one-time screening at different ages, and screening at different intervals and age ranges. Optimal screening strategies were identified depending on age and individual QoL preferences. Strategies were additionally evaluated with active surveillance by biennial re-biopsy delaying treatment of localized cancer until grade progression to Gleason score ≥ 7.ResultsScreening men with average PCa risk reduced quality-adjusted life expectancy (QALE) even under favorable assumptions. Men with elevated familial risk, depending on age and disutilities, gained QALE. While for men with familial risk aged 55 and 60 years annual screening to age 69 was the optimal strategy over most disutility ranges, no screening was the preferred option for 65 year-old men with average and above disutilities.Active surveillance greatly reduced overtreatment, but QALE gains by averted adverse events were opposed by losses due to delayed treatment and additional biopsies. The effect of active surveillance on the benefit-harm balance of screening differed between populations, as net losses and gains in QALE predicted for screening without active surveillance in men with average and familial PCa risk, respectively, were both reduced.ConclusionsAssumptions about PCa risk and screen-detectable prevalence significantly affect the benefit-harm balance of screening. Based on the assumptions of our model, PCa screening should focus on candidates with familial predisposition with consideration of individual QoL preferences and age. Active surveillance may require treatment initiation before Gleason score progression to 7. Alternative active surveillance strategies should be evaluated in further modeling studies.
“…1 and Table 1. Further details of the model, including its calibration and validation have been described earlier [41]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…to exit distant G < 7 cancer state (p / scale / shape) a
0.999 / 0.254 / 5.373calibrated [41] Prob. to exit distant G = 7 cancer state (p / scale / shape) a
0.945 / 0.806 / 4.564calibrated [41] Prob. to exit distant G > 7 cancer state (p / scale / shape) a
0.999 / 1.135 / 5.521calibrated [41] Familial risk factor on PCa onset and progression functions a
1.423calibrated [41] Prob.…”
Section: Methodsmentioning
confidence: 99%
“…However, out of the more than 25 PCa screening models [15–41], developed since the early 1990s, only five [21, 29, 37, 40, 41] account for both QoL and overdiagnosis with inconclusive results. While three of the five models [21, 29, 37] including the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model predict gains in quality-adjusted life expectancy (QALE) by screening up to age 70 and above in men with average PCa risk, two models [40, 41] including the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model predict potential losses. Estimates from the Erasmus MISCAN model suggest that overdiagnosis among screen-detected cancers might be as high as 50% [37, 42, 43].…”
Section: Introductionmentioning
confidence: 99%
“…Estimates from the Erasmus MISCAN model suggest that overdiagnosis among screen-detected cancers might be as high as 50% [37, 42, 43]. Estimates from the ONCOTYROL PCOP Model indicate even higher percentages [41]. …”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the model was recalibrated to match data from autopsy studies as well. Recalibration to higher prevalence, assuming that latent PCa detectable at autopsy might be detectable by screening as well, resulted in a considerable increase in overdiagnosis and decline in screening sensitivity, which shifted the benefit-harm balance of screening from QALE gains to losses [41]. However, previous analyses with the PCOP Model primarily focused on the effect of prevalence assumptions on the benefit-harm balance of screening and did not represent a comprehensive benefit-harm analysis.…”
BackgroundA recent recalibration of the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) Model, assuming that latent prostate cancer (PCa) detectable at autopsy might be detectable by screening as well, resulted in considerable worsening of the benefit-harm balance of screening. In this study, we used the recalibrated model to assess the effects of familial risk, quality of life (QoL) preferences, age, and active surveillance.MethodsMen with average and elevated familial PCa risk were simulated in separate models, differing in familial risk parameters. Familial risk was assumed to affect PCa onset and progression simultaneously in the base-case, and separately in scenario analyses. Evaluated screening strategies included one-time screening at different ages, and screening at different intervals and age ranges. Optimal screening strategies were identified depending on age and individual QoL preferences. Strategies were additionally evaluated with active surveillance by biennial re-biopsy delaying treatment of localized cancer until grade progression to Gleason score ≥ 7.ResultsScreening men with average PCa risk reduced quality-adjusted life expectancy (QALE) even under favorable assumptions. Men with elevated familial risk, depending on age and disutilities, gained QALE. While for men with familial risk aged 55 and 60 years annual screening to age 69 was the optimal strategy over most disutility ranges, no screening was the preferred option for 65 year-old men with average and above disutilities.Active surveillance greatly reduced overtreatment, but QALE gains by averted adverse events were opposed by losses due to delayed treatment and additional biopsies. The effect of active surveillance on the benefit-harm balance of screening differed between populations, as net losses and gains in QALE predicted for screening without active surveillance in men with average and familial PCa risk, respectively, were both reduced.ConclusionsAssumptions about PCa risk and screen-detectable prevalence significantly affect the benefit-harm balance of screening. Based on the assumptions of our model, PCa screening should focus on candidates with familial predisposition with consideration of individual QoL preferences and age. Active surveillance may require treatment initiation before Gleason score progression to 7. Alternative active surveillance strategies should be evaluated in further modeling studies.
It is imperative to understand the effects of early detection and treatment of chronic diseases, such as prostate cancer, regarding incidence, overtreatment and mortality. Previous simulation models have emulated clinical trials, and relied on extensive assumptions on the natural history of the disease. In addition, model parameters were typically calibrated to a variety of data sources. We propose a model designed to emulate real‐life scenarios of chronic disease using a proxy for the diagnostic activity without explicitly modeling the natural history of the disease and properties of clinical tests. Our model was applied to Swedish nation‐wide population‐based prostate cancer data, and demonstrated good performance in terms of reconstructing observed incidence and mortality. The model was used to predict the number of prostate cancer diagnoses with a high or limited diagnostic activity between 2017 and 2060. In the long term, high diagnostic activity resulted in a substantial increase in the number of men diagnosed with lower risk disease, fewer men with metastatic disease, and decreased prostate cancer mortality. The model can be used for prediction of outcome, to guide decision‐making, and to evaluate diagnostic activity in real‐life settings with respect to overdiagnosis and prostate cancer mortality.
According to our data, rs3742330, rs4961280 and rs7813 qualify for potentially protective genetic variants against PCa progression. These variants were not shown to be associated with PCa risk.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
