TY - JOUR
T1 - The use of modeling to understand the impact of screening on US mortality
T2 - Examples from mammography and PSA testing
AU - Feuer, Eric J.
AU - Etzioni, Ruth
AU - Cronin, Kathleen A.
AU - Mariotto, Angela
PY - 2004/12
Y1 - 2004/12
N2 - Surveillance data represent a vital resource for understanding the impact of cancer control interventions on the population cancer burden. However, population cancer trends are a complex product of many factors, and estimating the contribution of any one of these factors can be challenging. Surveillance modeling is a technique for estimating the contribution of one or more interventions of interest to trends in disease incidence and mortality. In this article, we present several approaches to surveillance modeling of cancer screening interventions. We classify models as biological or epidemiological, depending on whether they model the full unobservable aspects of disease onset and progression, or models which reduce the complex process to simpler terms by summarizing portions of the disease process using mostly observed population level measures. We also describe differences between macrolevel models, microsimulation models and mechanistic models. We discuss procedures for model calibration and validation, and methods for presenting model results which are robust with respect to certain types of biased model estimates. As examples, we present several models of the impact of mammography screening on breast cancer mortality, and PSA screening on prostate cancer mortality. Both these examples are appropriate uses of surveillance modeling, even though for mammography there is extensive (although somewhat controversial) randomized trial evidence, whereas for PSA this biomarker has seen extensive use as a screening test prior to any controlled trial evidence of its efficacy. Some of the models presented here were developed as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network.
AB - Surveillance data represent a vital resource for understanding the impact of cancer control interventions on the population cancer burden. However, population cancer trends are a complex product of many factors, and estimating the contribution of any one of these factors can be challenging. Surveillance modeling is a technique for estimating the contribution of one or more interventions of interest to trends in disease incidence and mortality. In this article, we present several approaches to surveillance modeling of cancer screening interventions. We classify models as biological or epidemiological, depending on whether they model the full unobservable aspects of disease onset and progression, or models which reduce the complex process to simpler terms by summarizing portions of the disease process using mostly observed population level measures. We also describe differences between macrolevel models, microsimulation models and mechanistic models. We discuss procedures for model calibration and validation, and methods for presenting model results which are robust with respect to certain types of biased model estimates. As examples, we present several models of the impact of mammography screening on breast cancer mortality, and PSA screening on prostate cancer mortality. Both these examples are appropriate uses of surveillance modeling, even though for mammography there is extensive (although somewhat controversial) randomized trial evidence, whereas for PSA this biomarker has seen extensive use as a screening test prior to any controlled trial evidence of its efficacy. Some of the models presented here were developed as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network.
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U2 - 10.1191/0962280204sm376ra
DO - 10.1191/0962280204sm376ra
M3 - Article
C2 - 15587432
AN - SCOPUS:9444271756
SN - 0962-2802
VL - 13
SP - 421
EP - 442
JO - Statistical methods in medical research
JF - Statistical methods in medical research
IS - 6
ER -