The Oncotyrol Prostate Cancer Outcome and Policy Model - How Latent Prevalence Affects the Benefit-Harm Balance of Screening

Mühlberger, Nikolai; Heijnsdijk, Eveline A.M.; Kurzthaler, Christina; Iskandar, Rowan; Krahn, Murray; Bremner, Karen E.; Oberaigner, Wilhelm; Klocker, Helmut; Horninger, Wolfgang; Conrads-Frank, Annette; Sroczynski, Gaby; Siebert, Uwe

doi:10.1016/j.jval.2014.08.1843

Cited by 6 publications

(22 citation statements)

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“…For example, the MISCAN prostate model was replicated by independent researchers based on the reporting of all basic parameters in our papers. 24,25 We have also described how the MISCAN model prediction of the impact of breast cancer screening fulfils predictive validity. 26 In short, MISCAN model predictions for the impact of breast cancer screening on incidence, made in 1994 for a steady-state screening situation 27 , closely resemble the actual breast cancer incidence rates in 2010 in the Netherlands.…”

Section: Model Validationmentioning

confidence: 99%

The role of modelling in the policy decision making process for cancer screening: example of prostate specific antigen screening

Getaneh

Heijnsdijk

Koning

2019

Public Health Res Pract

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Although randomised controlled trials are the preferred basis for policy decisions on cancer screening, it remains difficult to assess all downstream effects of screening, particularly when screening options other than those in the specific trial design are being considered. Simulation models of the natural history of disease can play a role in quantifying harms and benefits of cancer screening scenarios. Recently, the US Preventive Services Task Force issued a C-recommendation on screening for prostate cancer for men aged 55-69 years, implying at least moderate certainty that the benefit is small. However, modelling based on data from the European Randomized study of Screening for Prostate Cancer, which included quality-of-life estimates, showed that the ratio between benefits and harms is better, and likely to be reasonable, for men screened between the ages of 55 and 63 years (i.e. by using an earlier stopping age than applied in the trial setting). This commentary article considers the importance of simulation modelling in the decision-making process for (prostate) cancer screening. The paper also explores whether the recently published Cluster Randomized Trial of PSA Testing for Prostate Cancer, a trial of a single prostate specific antigen (PSA) testing intervention in the UK, changes the evidence for regular PSA testing for men aged 55-63 years by replicating the trial using a simulation model.

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Section: Model Validationmentioning

confidence: 99%

The role of modelling in the policy decision making process for cancer screening: example of prostate specific antigen screening

Getaneh

Heijnsdijk

Koning

2019

Public Health Res Pract

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“…65 Similar models have been developed, for example, for screening for psychosis, 66 agerelated macular degeneration, 67 and prostate cancer. 68 Models are also potentially appropriate to consider the potential effects of issues such as selection, detection, lead time, overdiagnosis, sticky diagnosis, and slippery linkage. 69 Another advantage is that models can complement trials by simulating more complex questions than can be addressed in trials.…”

Section: The Possibility Of Simulating the Effects Of Screeningmentioning

confidence: 99%

To Screen or Not to Screen

Salmi

Coureau

Bailhache

et al. 2016

Mayo Clinic Proceedings

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Screening is the early detection of a latent disorder by a test to allow early intervention with the aim of improving prognosis. Individual and population perspectives on screening are perceived as opposing interests of patients and the population. In this article, we try to reconcile these perspectives. The individual perspective is based on the clinical experience of a better prognosis at early stages and patients with missed opportunities. In the population perspective, screening is based on a population-oriented, evidence-based model and addresses the acceptability and possible negative effects, including for people without the disorder. Known possible obstacles to a positive effect of screening include a short latent stage, lead time, overdiagnosis, lack of acceptability, poor performance of tests, and misclassification of outcome. Randomized trials of screening are challenging and need an adaptation of standards such as the Consolidated Standards of Reporting Trials (CONSORT). Simulating the effects of screening can allow the consideration of complex screening strategies and other options to help avoid biases related to treatment improvement and prevention success. Reconciling both perspectives is possible by considering that hypotheses underlying the former are prerequisites for the latter. From an evidence-based medicine and policy perspective, we suggest that recommending screening or prescribing a test is unethical if all possible obstacles are not documented by providing the best available evidence.

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“…Results were presented at scientific conferences in Miami (SMDM) [23] and Amsterdam (ISPOR). [24] A manuscript has been accepted for publication. [23] …”

Section: Benefit-harm Balance and Cost Effectiveness In Prostate Cancmentioning

confidence: 99%

“…Oncotyrol initiated three international and interdisciplinary Task Force Workshops that brought together international expertise to develop best practice recommendations within the context of personalized cancer medicine. [29] The Oncotyrol research team has also published several methodological milestone papers, including papers on HTA methods, [30] principles of personalized cancer medicine, [15] concepts of personalization in health economics, [10] model review and validation, [17,26,28] and cancer registry analyses, [31][32][33][34][35][36] as well as papers assessing specific health technologies, including medical and economic decision analyses for CML treatment, [18][19][20] benefit-harm analyses for personalized prostate cancer screening, [23,37] benefit and cost-effectiveness analysis on breast cancer test-treatment strategies. [16,27] Two members of the Oncotyrol team were invited to lead and participate in the ISPOR-SMDM Joint Modeling Good Research Practices Task Force, which are today regarded as an international standard for decision modeling.…”

Section: Highlightsmentioning

confidence: 99%

Oncotyrol – Center for Personalized Cancer Medicine: Methods and Applications of Health Technology Assessment and Outcomes Research

Siebert

Jahn

Rochau

et al. 2015

Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheit

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The Oncotyrol Prostate Cancer Outcome and Policy Model - How Latent Prevalence Affects the Benefit-Harm Balance of Screening

Cited by 6 publications

References 0 publications

The role of modelling in the policy decision making process for cancer screening: example of prostate specific antigen screening

The role of modelling in the policy decision making process for cancer screening: example of prostate specific antigen screening

To Screen or Not to Screen

Oncotyrol – Center for Personalized Cancer Medicine: Methods and Applications of Health Technology Assessment and Outcomes Research

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