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Mühlberger N, Boskovic K, Krahn MD, Bremner KE, Oberaigner W, Klocker H, Horninger W, Sroczynski G, Siebert U. Benefits and harms of prostate cancer screening - predictions of the ONCOTYROL prostate cancer outcome and policy model. BMC Public Health 2017. [PMID: 28651567 PMCID: PMC5485506 DOI: 10.1186/s12889-017-4439-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background A 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. Methods Men 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. Results Screening 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. Conclusions Assumptions 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.
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Affiliation(s)
- Nikolai Mühlberger
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060, Hall i.T, Austria.,Division of Health Technology Assessment, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
| | - Kristijan Boskovic
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060, Hall i.T, Austria
| | - Murray D Krahn
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, ON, Canada.,Toronto General Research Institute, Toronto General Hospital, Toronto, ON, Canada
| | - Karen E Bremner
- Toronto General Research Institute, Toronto General Hospital, Toronto, ON, Canada
| | - Willi Oberaigner
- Cancer Registry of Tyrol, Tirol Kliniken GmbH, Innsbruck, Austria
| | - Helmut Klocker
- Department of Urology, Medical University of Innsbruck, Innsbruck, Austria
| | - Wolfgang Horninger
- Department of Urology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gaby Sroczynski
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060, Hall i.T, Austria.,Division of Health Technology Assessment, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
| | - Uwe Siebert
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060, Hall i.T, Austria. .,Division of Health Technology Assessment, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria. .,Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Mühlberger N, Kurzthaler C, Iskandar R, Krahn MD, Bremner KE, Oberaigner W, Klocker H, Horninger W, Conrads-Frank A, Sroczynski G, Siebert U. The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening. Med Decis Making 2015; 35:758-72. [PMID: 25977360 DOI: 10.1177/0272989x15585114] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 04/06/2015] [Indexed: 01/21/2023]
Abstract
BACKGROUND The ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model is a state-transition microsimulation model evaluating the benefits and harms of prostate cancer (PCa) screening. The natural history and detection component of the original model was based on the 2003 version of the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model, which was not calibrated to prevalence data. Compared with data from autopsy studies, prevalence of latent PCa assumed by the original model is low, which may bias the model toward screening. Our objective was to recalibrate the original model to match prevalence data from autopsy studies as well and compare benefit-harm predictions of the 2 model versions differing in prevalence. METHODS For recalibration, we reprogrammed the natural history and detection component of the PCOP model as a deterministic Markov state-transition cohort model in the statistical software package R. All parameters were implemented as variables or time-dependent functions and calibrated simultaneously in a single run. Observed data used as calibration targets included data from autopsy studies, cancer registries, and the European Randomized Study of Screening for Prostate Cancer. Compared models were identical except for calibrated parameters. RESULTS We calibrated 46 parameters. Prevalence from autopsy studies could not be fitted using the original parameter set. Additional parameters, allowing for interruption of disease progression and age-dependent screening sensitivities, were needed. Recalibration to higher prevalence demonstrated a considerable increase of overdiagnosis and decline of screening sensitivity, which significantly worsened the benefit-harm balance of screening. CONCLUSIONS 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.
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Affiliation(s)
- Nikolai Mühlberger
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US)
| | - Christina Kurzthaler
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US)
| | - Rowan Iskandar
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US),Department of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA (RI)
| | - Murray D Krahn
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, Ontario, Canada (MDK),Toronto General Research Institute, Toronto General Hospital, Toronto, Ontario, Canada (MDK, KEB)
| | - Karen E Bremner
- Toronto General Research Institute, Toronto General Hospital, Toronto, Ontario, Canada (MDK, KEB)
| | | | - Helmut Klocker
- Department of Urology, Innsbruck Medical University, Innsbruck, Austria (HK, WH)
| | - Wolfgang Horninger
- Department of Urology, Innsbruck Medical University, Innsbruck, Austria (HK, WH)
| | - Annette Conrads-Frank
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US)
| | - Gaby Sroczynski
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US)
| | - Uwe Siebert
- Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US),Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US),Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA (US),Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA (US)
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Garg V, Gu NY, Borrego ME, Raisch DW. A literature review of cost-effectiveness analyses of prostate-specific antigen test in prostate cancer screening. Expert Rev Pharmacoecon Outcomes Res 2014; 13:327-42. [PMID: 23763530 DOI: 10.1586/erp.13.26] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Prostate cancer is the most common non-skin cancer in American men, and prostate-specific antigen (PSA) testing is its common screening procedure. In May 2012, the US Preventive Services Task Force recommended against PSA-based screening. These recommendations contradict the current recommendations of other organizations such as the American Urological Association. The authors conducted a systematic review of PubMed, EMBASE and Cochrane to examine the published literature reporting the cost-effectiveness of PSA-based screening. The authors found ten studies each for US and non-US jurisdiction population. All reviewed studies concluded PSA-based screening to be cost effective in younger men (≤60 years of age) and at higher PSA levels (≥3 ng/ml). Further cost-effectiveness analyses reflecting latest clinical practice and current perspectives regarding adverse outcomes of potentially unnecessary treatment are required, especially from the US government perspective.
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Affiliation(s)
- Vishvas Garg
- Pharmacoeconomics, Epidemiology, Pharmaceutical Policy and Outcomes Research (PEPPOR) Program, Department of Pharmacy Practice and Administrative Sciences, College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
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Earle CC, Chapman RH, Baker CS, Bell CM, Stone PW, Sandberg EA, Neumann PJ. Systematic overview of cost-utility assessments in oncology. J Clin Oncol 2000; 18:3302-17. [PMID: 10986064 DOI: 10.1200/jco.2000.18.18.3302] [Citation(s) in RCA: 244] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Cost-utility analyses (CUAs) present the value of an intervention as the ratio of its incremental cost divided by its incremental survival benefit, with survival weighted by utilities to produce quality-adjusted life years (QALYs). We critically reviewed the CUA literature and its role in informing clinical oncology practice, research priorities, and policy. METHODS The English-language literature was searched between 1975 and1997 for CUAs. Two readers abstracted from each article descriptions of the clinical situation and patients, the methods used, study perspective, the measures of effectiveness, costs included, discounting, and whether sensitivity analyses were performed. The readers then made subjective quality assessments. We also extracted utility values from the reviewed papers, along with information on how and from whom utilities were measured. RESULTS Our search yielded 40 studies, which described 263 health states and presented 89 cost-utility ratios. Both the number and quality of studies increased over time. However, many studies are at variance with current standards. Only 20% of studies took a societal perspective, more than a third failed to discount both the costs and QALYs, and utilities were often simply estimates from the investigators or other physicians. CONCLUSION The cost-utility literature in oncology is not large but is rapidly expanding. There remains much room for improvement in the methodological rigor with which utilities are measured. Considering quality-of-life effects by incorporating utilities into economic studies is particularly important in oncology, where many therapies obtain modest improvements in response or survival at the expense of nontrivial toxicity.
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Affiliation(s)
- C C Earle
- Center for Outcomes and Policy Research, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
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