Literature DB >> 19779595

Applying the Lorenz curve to disease risk to optimize health benefits under cost constraints.

Mitchell H Gail1.   

Abstract

This paper shows how the Lorenz curve can be used, together with models of disease risk, to allocate scarce resources so as to optimize a health benefit. Consider the example of breast cancer mortality. If there were sufficient resources to provide all women with mammograms, a certain maximal number of lives could be saved. Suppose, however, that only a fraction of that amount of money is available for prevention activities. Suppose that a questionnaire could be given to assess a woman's risk of dying of breast cancer. Depending on the amount of money available, on the ratio of the cost of a questionnaire to the cost of a mammogram, and on the Lorenz curve of the distribution of risks of breast cancer mortality, I calculate the proportion of women who should be given questionnaires, the proportion of women given the questionnaires who should be given mammograms because they have high risks, and the proportion of women not given questionnaires who should be assigned to receive mammograms at random so as to maximize the number of lives saved.

Entities:  

Year:  2009        PMID: 19779595      PMCID: PMC2749326          DOI: 10.4310/sii.2009.v2.n2.a1

Source DB:  PubMed          Journal:  Stat Interface        ISSN: 1938-7989            Impact factor:   0.582


  5 in total

Review 1.  On the efficacy of screening for breast cancer.

Authors:  David A Freedman; Diana B Petitti; James M Robins
Journal:  Int J Epidemiol       Date:  2004-02       Impact factor: 7.196

2.  On criteria for evaluating models of absolute risk.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  Biostatistics       Date:  2005-04       Impact factor: 5.899

3.  Validation studies for models projecting the risk of invasive and total breast cancer incidence.

Authors:  J P Costantino; M H Gail; D Pee; S Anderson; C K Redmond; J Benichou; H S Wieand
Journal:  J Natl Cancer Inst       Date:  1999-09-15       Impact factor: 13.506

4.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.

Authors:  M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill
Journal:  J Natl Cancer Inst       Date:  1989-12-20       Impact factor: 13.506

5.  Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.

Authors:  Mitchell H Gail
Journal:  J Natl Cancer Inst       Date:  2008-07-08       Impact factor: 13.506

  5 in total
  7 in total

1.  Twenty-five years of breast cancer risk models and their applications.

Authors:  Mitchell H Gail
Journal:  J Natl Cancer Inst       Date:  2015-02-26       Impact factor: 13.506

2.  Assessing the incremental value of diagnostic and prognostic markers: a review and illustration.

Authors:  Ewout W Steyerberg; Michael J Pencina; Hester F Lingsma; Michael W Kattan; Andrew J Vickers; Ben Van Calster
Journal:  Eur J Clin Invest       Date:  2011-07-05       Impact factor: 4.686

3.  Using multiple risk models with preventive interventions.

Authors:  Mitchell H Gail
Journal:  Stat Med       Date:  2012-06-26       Impact factor: 2.373

4.  Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  J Natl Cancer Inst       Date:  2018-09-01       Impact factor: 13.506

5.  Personalized estimates of breast cancer risk in clinical practice and public health.

Authors:  Mitchell H Gail
Journal:  Stat Med       Date:  2011-02-21       Impact factor: 2.373

6.  Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model.

Authors:  Mitchell H Gail
Journal:  J Natl Cancer Inst       Date:  2009-06-17       Impact factor: 13.506

7.  Robustness of risk-based allocation of resources for disease prevention.

Authors:  Mitchell H Gail; David Pee
Journal:  Stat Methods Med Res       Date:  2020-06-17       Impact factor: 3.021

  7 in total

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