Chapter 13: A Comparative Review of CISNET Breast Models Used To Analyze U.S. Breast Cancer Incidence and Mortality Trends

MS, Cornerstone Systems Northwest Inc, Lynden, WA 98264, USA.
JNCI Monographs 02/2006; DOI: 10.1093/jncimonographs/lgj013
Source: PubMed

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.

  • Source
    • "By taking these characteristics into account, it is possible to examine a likely range of outcomes, including extent of illness, mortality and associated costs. Over the past decade, the NCI CISNET program has supported the development of models to examine how changes in screening and treatment have influenced breast cancer mortality (Boer et al. 2004; Clarke et al. 2006; Mandelblatt et al. 2003, 2009; Tosteson et al. 2008). We will adapt the CISNET Spectrum model to examine net improvements in breast cancer outcomes associated with improvement of adherence to screening guidelines and levels of intervention performance achieved by our project. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Dissemination efforts must optimize interventions for new settings and populations. As such, dissemination research should incorporate principles of quality improvement. Comprehensive Dynamic Trial (CDT) designs examine how information gained during dissemination may be used to modify interventions and improve performance. Although CDT may offer distinct advantages over static designs, organizing the many necessary roles and activities is a significant challenge. In this article, we discuss use of the Interactive Systems Framework for Dissemination and Implementation to systematically implement a CDT. Specifically, we describe "Bronx ACCESS", a program designed to disseminate evidence-based strategies to promote adherence to mammography guidelines. In Bronx ACCESS, the Intervention Delivery System will elicit information needed to adapt strategies to specific settings and circumstances. The Intervention Synthesis and Translation System will use this information to test changes to strategies through "embedded experiments". The Intervention Support System will build local capacities found to be necessary for intervention institutionalization. Simulation modeling will be used to integrate findings across systems. Results will inform on-going policy debate about interventions needed to promote population-level screening. More generally, this project is intended to advance understanding of research paradigms necessary to study dissemination.
    American Journal of Community Psychology 05/2012; 50(3-4). DOI:10.1007/s10464-012-9518-6 · 1.74 Impact Factor
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Increasingly, computer simulation models are used for economic and policy evaluation in cancer prevention and control. A model’s predictions of key outcomes, such as screening effectiveness, depend on the values of unobservable natural history parameters. Calibration is the process of determining the values of unobservable parameters by constraining model output to replicate observed data. Because there are many approaches for model calibration and little consensus on best practices, we surveyed the literature to catalogue the use and reporting of these methods in cancer simulation models. We conducted a MEDLINE search (1980 through 2006) for articles on cancer-screening models and supplemented search results with articles from our personal reference databases. For each article, two authors independently abstracted pre-determined items using a standard form. Data items included cancer site, model type, methods used for determination of unobservable parameter values and description of any calibration protocol. All authors reached consensus on items of disagreement. Reviews and non-cancer models were excluded. Articles describing analytical models, which estimate parameters with statistical approaches (e.g. maximum likelihood) were catalogued separately.Models that included unobservable parameters were analysed and classified by whether calibration methods were reported and if so, the methods used. The review process yielded 154 articles that met our inclusion criteria and, of these, we concluded that 131 may have used calibration methods to determine model parameters. Although the term ‘calibration’ was not always used, descriptions of calibration or ‘model fitting’ were found in 50% (n = 66) of the articles, with an additional 16% (n = 21) providing a reference to methods. Calibration target data were identified in nearly all of these articles. Other methodological details, such as the goodness-of-fit metric, were discussed in 54% (n = 47 of 87) of the articles reporting calibration methods, while few details were provided on the algorithms used to search the parameter space. Our review shows that the use of cancer simulation modelling is increasing, although thorough descriptions of calibration procedures are rare in the published literature for these models. Calibration is a key component of model development and is central to the validity and credibility of subsequent analyses and inferences drawn from model predictions. To aid peer-review and facilitate discussion of modelling methods, we propose a standardized Calibration Reporting Checklist for model documentation.
    PharmacoEconomics 02/2009; 27(7):533-45. DOI:10.2165/11314830-000000000-00000 · 3.34 Impact Factor
Show more