| Description |
1 online resource |
| Series |
Monographs on Statistics & Applied Probability ; 154 |
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A Chapman & Hall book |
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Monographs on statistics and applied probability (Series) ; 154.
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| Contents |
880-01 Cover; Half Title; Title Page; Copyright Page; Table of Contents; List of Figures; List of Tables; Symbols; Preface; 1: Introduction; 1.1 Examples of risk models for disease incidence; 1.1.1 Breast cancer incidence; 1.1.1.1 A brief survey of models; 1.1.1.2 The National Cancer Institute's (NCI's) Breast Cancer Risk Assessment Tool, BCRAT; 1.1.2 Other models of cancer incidence; 1.1.3 Framingham Model for incidence of coronary heart disease; 1.2 Applications of risk models for disease incidence; 1.3 Prognosis after disease diagnosis; 1.4 Contents of book |
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880-01/(S 4.6.2 Influence function based variance of the absolute risk estimate from cohort data5: Estimating absolute risk by combining case-control or cohort data with disease registry data; 5.1 Relationship between attributable risk, composite age-specific incidence, and baseline hazard; 5.2 Estimating relative risk and attributable risk from case-control data; 5.3 Estimating relative risk and attributable risk from cohort data; 5.4 Estimating the cause-specific hazard of the competing causes of mortality, λ2(t; z2); 5.5 Some strengths and limitations of using registry data |
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2: Definitions and basic concepts for survival data in a cohort without covariates2.1 Basic survival concepts; 2.2 Choice of time scale: age, time since diagnosis, time since accrual or counseling; 2.3 Censoring; 2.3.1 Right censoring; 2.4 Truncation; 2.5 Life-table estimator; 2.5.1 Kaplan-Meier survival estimate; 2.6 Counting processes and Markov methods; 3: Competing risks; 3.1 Concepts and definitions; 3.2 Pure versus cause-specific hazard functions; 3.3 Non-parametric estimation of absolute risk; 4: Regression models for absolute risk estimated from cohort data |
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4.1 Cause-specific hazard regression4.1.1 Estimation of the hazard ratio parameters; 4.1.2 Non-parametric estimation of the baseline hazard; 4.1.3 Semi-parametric estimation of absolute risk rm; 4.1.4 Estimation of a piecewise exponential baseline hazard model; 4.1.5 Alternative hazard models; 4.2 Cumulative incidence regression; 4.2.1 Proportional sub-distribution hazards model; 4.2.2 Other cumulative incidence regression models; 4.2.3 Relationship between the cause-specific and the proportional sub-distribution hazards models; 4.3 Examples; 4.3.1 Absolute risk of breast cancer incidence |
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4.3.2 Absolute risk of second primary thyroid cancer (SPTC) incidence4.4 Estimating cause-specific hazard functions from sub-samples from cohorts; 4.4.1 Case-cohort design; 4.4.2 Nested case-control design; 4.5 Estimating cause specific hazard functions from cohorts with complex survey designs; 4.5.1 Example of survey design; 4.5.2 Data; 4.5.3 Estimation of hazard ratio parameters and the baseline hazard function; 4.5.4 Example: absolute risk of cause-specific deaths from the NHANES I and II; 4.6 Variance estimation; 4.6.1 Approaches to variance estimation |
| Summary |
"Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk. Features:Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate modelsProvides details on statistical inference for the various sampling designsDiscusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies Discusses model updating, family-based designs, dynamic projections, and other topicsRuth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association, with interests in risk modeling, dimension reduction, and applications in epidemiology. She developed absolute risk models for breast cancer, colon cancer, melanoma, and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used "Gail model" for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health."--Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Health risk assessment.
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Public health -- Risk assessment
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Risk Assessment -- methods
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Health Status Indicators
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MEDICAL -- Epidemiology.
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TECHNOLOGY & ENGINEERING -- Environmental -- General.
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POLITICAL SCIENCE -- Public Policy -- Social Security.
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POLITICAL SCIENCE -- Public Policy -- Social Services & Welfare.
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Health risk assessment.
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| Form |
Electronic book
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| Author |
Gail, Mitchell H
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| ISBN |
9781315117539 |
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1315117533 |
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9781351643818 |
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1351643819 |
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