Simulation models in population breast cancer screening: A systematic review

TY - JOUR

T1 - Simulation models in population breast cancer screening

T2 - A systematic review

AU - Koleva-Kolarova, Rositsa G

AU - Zhan, Zhuozhao

AU - Greuter, Marcel J W

AU - Feenstra, Talitha L

AU - De Bock, Geertruida H

N1 - Copyright © 2015 Elsevier Ltd. All rights reserved.

PY - 2015/8

Y1 - 2015/8

N2 - The aim of this review was to critically evaluate published simulation models for breast cancer screening of the general population and provide a direction for future modeling. A systematic literature search was performed to identify simulation models with more than one application. A framework for qualitative assessment which incorporated model type; input parameters; modeling approach, transparency of input data sources/assumptions, sensitivity analyses and risk of bias; validation, and outcomes was developed. Predicted mortality reduction (MR) and cost-effectiveness (CE) were compared to estimates from meta-analyses of randomized control trials (RCTs) and acceptability thresholds. Seven original simulation models were distinguished, all sharing common input parameters. The modeling approach was based on tumor progression (except one model) with internal and cross validation of the resulting models, but without any external validation. Differences in lead times for invasive or non-invasive tumors, and the option for cancers not to progress were not explicitly modeled. The models tended to overestimate the MR (11-24%) due to screening as compared to optimal RCTs 10% (95% CI - 2-21%) MR. Only recently, potential harms due to regular breast cancer screening were reported. Most scenarios resulted in acceptable cost-effectiveness estimates given current thresholds. The selected models have been repeatedly applied in various settings to inform decision making and the critical analysis revealed high risk of bias in their outcomes. Given the importance of the models, there is a need for externally validated models which use systematical evidence for input data to allow for more critical evaluation of breast cancer screening.

AB - The aim of this review was to critically evaluate published simulation models for breast cancer screening of the general population and provide a direction for future modeling. A systematic literature search was performed to identify simulation models with more than one application. A framework for qualitative assessment which incorporated model type; input parameters; modeling approach, transparency of input data sources/assumptions, sensitivity analyses and risk of bias; validation, and outcomes was developed. Predicted mortality reduction (MR) and cost-effectiveness (CE) were compared to estimates from meta-analyses of randomized control trials (RCTs) and acceptability thresholds. Seven original simulation models were distinguished, all sharing common input parameters. The modeling approach was based on tumor progression (except one model) with internal and cross validation of the resulting models, but without any external validation. Differences in lead times for invasive or non-invasive tumors, and the option for cancers not to progress were not explicitly modeled. The models tended to overestimate the MR (11-24%) due to screening as compared to optimal RCTs 10% (95% CI - 2-21%) MR. Only recently, potential harms due to regular breast cancer screening were reported. Most scenarios resulted in acceptable cost-effectiveness estimates given current thresholds. The selected models have been repeatedly applied in various settings to inform decision making and the critical analysis revealed high risk of bias in their outcomes. Given the importance of the models, there is a need for externally validated models which use systematical evidence for input data to allow for more critical evaluation of breast cancer screening.

KW - Algorithms

KW - Bias

KW - Breast Neoplasms/diagnosis

KW - Cost-Benefit Analysis

KW - Early Detection of Cancer/economics

KW - Female

KW - Humans

KW - Mass Screening/methods

KW - Models, Theoretical

KW - Sensitivity and Specificity

U2 - 10.1016/j.breast.2015.03.013

DO - 10.1016/j.breast.2015.03.013

M3 - Review article

C2 - 25906671

SN - 0960-9776

VL - 24

SP - 354

EP - 363

JO - Breast (Edinburgh, Scotland)

JF - Breast (Edinburgh, Scotland)

IS - 4

ER -