Cancer prevention and screening / / edited by Rosalind A. Eeles, Jeffrey S. Tobias, Christine D. Berg

• Record Nr.: UNINA9910555288903321
• Pubbl/distr/stampa: Hoboken, NJ : , : Wiley, , 2019
• ISBN: 9781118991060; 1-118-99106-0; 1-118-99102-8; 1-118-99095-1
• Descrizione fisica: 1 online resource (456 pages)
• Disciplina: 616.99/4052
• Soggetti: Medical screening; Patient education; Cancer - Diagnosis
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Title Page -- Copyright Page -- Contents -- List of contributors -- Foreword -- Prologue -- Chapter 1 Global perspectives surrounding cancer prevention and screening -- Principles of cancer control strategy -- Magnitude of the problem: Proportion of cancer globally attributable to preventable causes -- Primary prevention strategies globally -- Tobacco smoking -- Vaccination -- Screening -- General principles -- Existing screening options -- The harms of screening -- Targeted screening -- Early diagnosis -- Historical importance: Reasons for late presentation -- Targeted screening based on nonspecific symptoms -- Conclusion. CPAC's role in cancer screeningConclusion -- References -- Chapter 3 Cancer screening: A general perspective -- On the nature of screening -- Assessing a screening test -- Assessing benefit from a screening test -- Lead-time bias -- Length bias -- Overdiagnosis -- The order of evidence of benefit -- The population screened -- The magnitude of screening benefit -- Screening in practice -- Conclusion -- References -- Chapter 4 The balance of cancer screening risks and benefits -- Cervical screening -- Breast screening -- Colorectal cancer screening ; -- Prostate cancer screening -- Psychological harms of screening. Informed decision-makingConclusion -- References -- Chapter 5 Cancer screening issues in black and ethnic minority populations -- Conclusion -- References -- Chapter 6 Public awareness of cancer screening -- Awareness of cancer screening -- Beliefs relevant to cancer screening -- Perceptions of cancer risk -- Cancer fear -- Cancer fatalism -- Perceived benefits and harms of cancer screening -- Informed choice -- Intention and action -- Engagement -- Conclusion -- References -- Chapter 7 Public understanding of cancer prevention -- Public understanding of prevention -- What can we do to improve understanding? Case study: Online informationCase study: The media -- Case study: The Cancer Awareness Roadshow, a face-to-face intervention -- Case study: Talk Cancer, training for health workers -- Communicating risk -- Conclusion -- References -- Chapter 8 Cervical cancer screening: An exemplar of a population screening programme, and cervical cancer prevention -- How do we screen for cervical precancer? -- Prevention of invasive cervical cancer: The impact of cytological screening -- Factors influencing the success of a screening programme -- Molecular-based cervical screening technologies.
• Sommario/riassunto: "Cancer Prevention and Screening offers physicians and all clinical healthcare professionals a comprehensive, useful source of the latest information on cancer screening and prevention with both a global and a multidisciplinary perspective. Includes background information on epidemiology, cancer prevention, and cancer screening, for quick reference Offers the latest information for clinical application of the most recent techniques in prevention and screening of all major and many lesser cancer types Emphasises the importance of multidisciplinary teamwork in cancer screening Highlights frequent dilemmas and difficulties encountered during cancer screening Provides clear-cut clinical strategies for optimal patient education, communication, and compliance with cancer prevention techniques"--Provided by publisher.

Forecasting with dynamic regression models [[electronic resource] /] / Alan Pankratz

• Record Nr.: UNINA9910829964503321
• Autore: Pankratz Alan <1944->
• Pubbl/distr/stampa: New York, : John Wiley & Sons, 1991
• ISBN: 1-283-44612-X; 9786613446121; 1-118-15052-X; 1-118-15078-3
• Descrizione fisica: 1 online resource (410 p.)
• Collana: Wiley series in probability and mathematical statistics. Applied probability and statistics, , 0271-6356
• Disciplina: 519.5/5; 519.55
• Soggetti: Time-series analysis; Regression analysis; Prediction theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "A Wiley-Interscience publication."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Forecasting with Dynamic Regression Models; Contents; Preface; Chapter 1 Introduction and Overview; 1.1 Related Time Series; 1.2 Overview: Dynamic Regression Models; 1.3 Box and Jenkins' Modeling Strategy; 1.4 Correlation; 1.5 Layout of the Book; Questions and Problems; Chapter 2 A Primer on ARIMA Models; 2.1 Introduction; 2.2 Stationary Variance and Mean; 2.3 Autocorrelation; 2.4 Five Stationary ARIMA Processes; 2.5 ARIMA Modeling in Practice; 2.6 Backshift Notation; 2.7 Seasonal Models; 2.8 Combined Nonseasonal and Seasonal Processes; 2.9 Forecasting; 2.10 Extended Autocorrelation Function; 2.11 Interpreting ARIMA Model ForecastsQuestions and Problems; Case 1 Federal Government Receipts (ARIMA); Chapter 3 A Primer on Regression Models; 3.1 Two Types of Data; 3.2 The Population Regression Function (PRF) with One Input; 3.3 The Sample Regression Function (SRF) with One Input; 3.4 Properties of the Least-Squares Estimators; 3.5 Goodness of Fit (R2); 3.6 Statistical Inference; 3.7 ; Multiple Regression; 3.8 Selected Issues in Regression; 3.9 Application to Time Series Data; Questions and Problems; Case 2 Federal Government Receipts (Dynamic Regression); Case 3 Kilowatt-Hours Used; Chapter 4 Rational Distributed Lag Models4.1 Linear Distributed Lag Transfer Functions; 4.2 A Special Case: The Koyck Model; 4.3 Rational Distributed Lags; 4.4 The Complete Rational Form DR Model and Some Special Cases 163; Questions and Problems; Chapter 5 Building Dynamic Regression Models: Model Identification; 5.1 Overview; 5.2 Preliminary Modeling Steps; 5.3 The Linear Transfer Function (LTF) Identification Method; 5.4 Rules for Identifying Rational Distributed Lag Transfer Functions; Questions and Problems; Appendix 5A The Corner Table; Appendix 5B Transfer Function Identification Using Prewhitening and Cross CorrelationsChapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation; 6.1 Diagnostic Checking and Model Reformulation; 6.2 Evaluating Estimation Stage Results; Questions and Problems; Case 4 Housing Starts and Sales; Case 5 Industrial Production, Stock Prices, and Vendor Performance; Chapter 7 Intervention Analysis; 7.1 Introduction; 7.2 Pulse Interventions; 7.3 Step Interventions; 7.4 Building Intervention Models; 7.5 Multiple and Compound Interventions; Questions and Problems; Case 6 Year-End LoadingChapter 8 Intervention and Outlier Detection and Treatment; 8.1 The Rationale for Intervention and Outlier Detection; 8.2 Models for Intervention and Outlier Detection; 8.3 Likelihood Ratio Criteria; 8.4 An Iterative Detection Procedure; 8.5 Application; 8.6 Detected Events Near the End of a Series; Questions and Problems; Appendix 8A BASIC Program to Detect AO, LS, and IO Events; Appendix 8B Specifying IO Events in the SCA System; Chapter 9 Estimation and Forecasting; 9.1 DR Model Estimation; 9.2 Forecasting; Questions and Problems; Appendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24)
• Sommario/riassunto: One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.