A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends.
ABSTRACT The CISNET Breast Cancer program is a National Cancer Institute-sponsored collaboration composed of seven research groups that have modeled the impact of screening and adjuvant treatment on trends in breast cancer incidence and mortality over the period 1975-2000 (base case). This collaboration created a unique opportunity to make direct comparison of results from different models of population-based cancer screening produced in response to the same question. Comparing results in all but the most cursory way necessitates comparison of the models themselves. Previous chapters have discussed the models individual in detail. This chapter will aid the reader in understanding key areas of difference between the models. A focused analysis of differences and similarities between the models is presented with special attention paid to areas deemed most likely to contribute substantially to the results of the target analysis.
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ABSTRACT: Background A number of models exploring the cost-effectiveness of dabigatran versus warfarin for stroke prevention in atrial fibrillation have been published. These studies found dabigatran was generally cost-effective, considering well-accepted willingness-to-pay thresholds, but estimates of the incremental cost-effectiveness ratios (ICERs) varied, even in the same setting. The objective of this study was to compare the findings of the published economic models and identify key model features accounting for differences. Methods All aspects of the economic evaluations were reviewed: model approach, inputs, and assumptions. A previously published model served as the reference model for comparisons of the selected studies in the US and UK settings. The reference model was adapted, wherever possible, using the inputs and key assumptions from each of the other published studies to determine if results could be reproduced in the reference model. Incremental total costs, incremental quality-adjusted life years (QALYs), and ICERs (cost per QALY) were compared between each study and the corresponding adapted reference model. The impact of each modified variable or assumption was tracked separately. Results The selected studies were in the US setting (2), the Canadian setting (1), and the UK setting (2). All models used the Randomized Evaluation of Long-Term Anticoagulation study (RE-LY) as the main source for clinical inputs, and all used a Markov modelling approach, except one that used discrete event simulation. The reference model had been published in the Canadian and UK settings. In the UK setting, the reference model reported an ICER of UK£4,831, whereas the other UK-based analysis reported an ICER of UK£23,082. When the reference model was modified to use the same population characteristics, cost inputs, and utility inputs, it reproduced the results of the other model (ICER UK£25,518) reasonably well. Key reasons for the different results between the two models were the assumptions on the event utility decrement and costs associated with intracranial haemorrhage, as well as the costs of warfarin monitoring and disability following events. In the US setting, the reference model produced an ICER similar to the ICER from one of the US models (US$15,115/QALY versus US$12,386/QALY, respectively) when modelling assumptions and input values were transferred into the reference model. Key differences in results could be explained by the population characteristics (age and baseline stroke risk), utility assigned to events and specific treatments, adjustment of stroke and intracranial haemorrhage risk over time, and treatment discontinuation and switching. The reference model was able to replicate the QALY results, but not the cost results, reported by the other US cost-effectiveness analysis. The parameters driving the QALY results were utility values by disability levels as well as utilities assigned to specific treatments, and event and background mortality rates. Conclusions Despite differences in model designs and structures, it was mostly possible to replicate the results published by different authors and identify variables responsible for differences between ICERs using a reference model approach. This enables a better interpretation of published findings by focusing attention on the assumptions underlying the key model features accounting for differences.PharmacoEconomics 04/2013; 31(7). DOI:10.1007/s40273-013-0035-8 · 3.34 Impact FactorThis article is viewable in ResearchGate's enriched formatRG Format enables you to read in context with side-by-side figures, citations, and feedback from experts in your field.
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ABSTRACT: Like any other medical technology or intervention, diagnostic tests should be thoroughly evaluated before their introduction into daily practice. Increasingly, decision makers, physicians, and other users of diagnostic tests request more than simple measures of a test's analytical or technical performance and diagnostic accuracy; they would also like to see testing lead to health benefits. In this last article of our series, we introduce the notion of clinical utility, which expresses-preferably in a quantitative form-to what extent diagnostic testing improves health outcomes relative to the current best alternative, which could be some other form of testing or no testing at all. In most cases, diagnostic tests improve patient outcomes by providing information that can be used to identify patients who will benefit from helpful downstream management actions, such as effective treatment in individuals with positive test results and no treatment for those with negative results. We describe how comparative randomized clinical trials can be used to estimate clinical utility. We contrast the definition of clinical utility with that of the personal utility of tests and markers. We show how diagnostic accuracy can be linked to clinical utility through an appropriate definition of the target condition in diagnostic-accuracy studies.Clinical Chemistry 06/2012; DOI:10.1373/clinchem.2012.182576 · 7.77 Impact Factor
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ABSTRACT: BACKGROUND: US breast cancer mortality is declining, but thousands of women still die each year. METHODS: Two established simulation models examine 6 strategies that include increased screening and/or treatment or elimination of obesity versus continuation of current patterns. The models use common national data on incidence and obesity prevalence, competing causes of death, mammography characteristics, treatment effects, and survival/cure. Parameters are modified based on obesity (defined as BMI ≥ 30 kg/m(2) ). Outcomes are presented for the year 2025 among women aged 25+ and include numbers of cases, deaths, mammograms and false-positives; age-adjusted incidence and mortality; breast cancer mortality reduction and deaths averted; and probability of dying of breast cancer. RESULTS: If current patterns continue, the models project that there would be about 50,100-57,400 (range across models) annual breast cancer deaths in 2025. If 90% of women were screened annually from ages 40 to 54 and biennially from ages 55 to 99 (or death), then 5100-6100 fewer deaths would occur versus current patterns, but incidence, mammograms, and false-positives would increase. If all women received the indicated systemic treatment (with no screening change), then 11,400-14,500 more deaths would be averted versus current patterns, but increased toxicity could occur. If 100% received screening plus indicated therapy, there would be 18,100-20,400 fewer deaths. Eliminating obesity yields 3300-5700 fewer breast cancer deaths versus continuation of current obesity levels. CONCLUSIONS: Maximal reductions in breast cancer deaths could be achieved through optimizing treatment use, followed by increasing screening use and obesity prevention. Cancer 2013;. © 2013 American Cancer Society.Cancer 04/2013; DOI:10.1002/cncr.28087 · 5.20 Impact Factor