Literature DB >> 17725809

Mortality modeling of early detection programs.

Sandra J Lee1, Marvin Zelen.   

Abstract

Consider a group of subjects who are offered an opportunity to receive a sequence of periodic special examinations for the purpose of diagnosing a chronic disease earlier relative to usual care. The mortality for the early detection group is to be compared with a group receiving usual care. Benefit is reflected in a potential reduction in mortality. This article develops a general probability model that can be used to predict cumulative mortality for each of these groups. The elements of the model assume (i) a four-state progressive disease model in which a subject may be in a disease-free state (or a disease state that cannot be detected), preclinical disease state (capable of being diagnosed by a special exam), clinical state (diagnosis by usual care), and a death state; (ii) age-dependent transitions into the states; (iii) age-dependent examination sensitivity; (iv) age-dependent sojourn time in each state; and (v) the distribution of disease stages on diagnosis conditional on modality of detection. The model may be used to (i) compare mortality rates for different screening schedules; (ii) explore potential benefit of subpopulations; and (iii) compare relative reductions in disease-specific mortality due to advances and dissemination of both treatment and early detection screening programs.

Mesh:

Year:  2007        PMID: 17725809     DOI: 10.1111/j.1541-0420.2007.00893.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  9 in total

1.  The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update.

Authors:  Sandra J Lee; Xiaoxue Li; Hui Huang; Marvin Zelen
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

2.  Breast cancer incidence and overdiagnosis in Catalonia (Spain).

Authors:  Montserrat Martinez-Alonso; Ester Vilaprinyo; Rafael Marcos-Gragera; Montserrat Rue
Journal:  Breast Cancer Res       Date:  2010-08-03       Impact factor: 6.466

3.  Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.

Authors:  Elliot Lee; Mariel S Lavieri; Michael L Volk; Yongcai Xu
Journal:  Health Care Manag Sci       Date:  2014-10-12

4.  Cost-effectiveness of early detection of breast cancer in Catalonia (Spain).

Authors:  Misericordia Carles; Ester Vilaprinyo; Francesc Cots; Aleix Gregori; Roger Pla; Rubén Román; Maria Sala; Francesc Macià; Xavier Castells; Montserrat Rue
Journal:  BMC Cancer       Date:  2011-05-23       Impact factor: 4.430

5.  Contribution of early detection and adjuvant treatments to breast cancer mortality reduction in Catalonia, Spain.

Authors:  Ester Vilaprinyo; Teresa Puig; Montserrat Rue
Journal:  PLoS One       Date:  2012-01-17       Impact factor: 3.240

6.  Comparison of surveillance-based metrics for the assessment and monitoring of disease detection: simulation study about type 2 diabetes.

Authors:  Ralph Brinks; Annika Hoyer; Deborah B Rolka; Oliver Kuss; Edward W Gregg
Journal:  BMC Med Res Methodol       Date:  2017-04-11       Impact factor: 4.615

7.  Effectiveness of early detection on breast cancer mortality reduction in Catalonia (Spain).

Authors:  Montserrat Rue; Ester Vilaprinyo; Sandra Lee; Montserrat Martinez-Alonso; Misericor-Dia Carles; Rafael Marcos-Gragera; Roger Pla; Josep-Alfons Espinas
Journal:  BMC Cancer       Date:  2009-09-15       Impact factor: 4.430

8.  Cost-effectiveness and harm-benefit analyses of risk-based screening strategies for breast cancer.

Authors:  Ester Vilaprinyo; Carles Forné; Misericordia Carles; Maria Sala; Roger Pla; Xavier Castells; Laia Domingo; Montserrat Rue
Journal:  PLoS One       Date:  2014-02-03       Impact factor: 3.240

9.  Modeling the natural history of ductal carcinoma in situ based on population data.

Authors:  Sarocha Chootipongchaivat; Nicolien T van Ravesteyn; Xiaoxue Li; Hui Huang; Harald Weedon-Fekjær; Marc D Ryser; Donald L Weaver; Elizabeth S Burnside; Brandy M Heckman-Stoddard; Harry J de Koning; Sandra J Lee
Journal:  Breast Cancer Res       Date:  2020-05-27       Impact factor: 6.466

  9 in total

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