Probability modeling and statistical inference in cancer screening / Dongfeng Wu.

Format:

Book

Author/Creator:

Wu, Dongfeng (College teacher), author.

Contributor:

Taylor & Francis eBooks

Rosengarten Family Fund.

Series:

Chapman & Hall/CRC biostatistics series

Language:

English

Subjects (All):

Cancer--Diagnosis--Statistical methods.

Cancer.

Cancer--Diagnosis--Mathematical models.

Biometry.

Medical Subjects:

Biometry.

Physical Description:

1 online resource (xxiii, 261 pages) : illustrations.

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2024.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Dedication

Contents

Preface

List of Figures

List of Tables

Symbols

1. A Brief Review of Probability and Examples of Screening Data

1.1. Sample space, event, and probability

1.2. Random variable and distribution function

1.3. Expectation, moments, and correlation

1.4. Frequentist statistics and Bayes inference

1.5. Markov Chain Monte Carlo algorithms

1.6. Screening data format

1.7. The Minnesota Colorectal Cancer Study

1.8. The Mayo Lung Project

1.9. The Health Insurance Plan (HIP) of Greater New York

1.10. The National Lung Screening Trial (NLST) study

2. Estimating the Three Key Parameters

2.1. Introduction

2.2. The three key parameters and other terminology

2.3. Probability calculation

2.3.1. Probability of incidence and lifetime risk

2.3.2. Probability of screen-detected cases

2.3.3. Probability of interval incidence cases

2.4. Sensitivity as a function of age

2.4.1. Probability formulation and the likelihood

2.4.2. Simulation: Checking model reliability

2.4.3. Application: The HIP for breast cancer

2.4.4. Application: The Minnesota Colorectal Cancer study

2.4.5. Application: The Mayo Lung Project

2.4.6. Application: The National Lung Screening Trial

2.5. Sensitivity as a function of time in the Sp and sojourn time

2.5.1. Probability formulation and likelihood

2.5.2. Application: The NLST-CT for heavy smokers

2.6. An open problem

2.7. Bibliographic notes

2.8. Solution for some exercises

3. Testing Dependency of Two Screening Modalities

3.1. Introduction

3.2. Data format for two screening modalities

3.3. Testing dependency under the stable disease model

3.3.1. The likelihood function

3.3.2. Application: Mammogram and clinical breast exam in the HIP study

3.4. Testing dependency under the non-stable disease model

3.4.1. The likelihood function

3.4.2. Application: Chest X-ray and sputum cytology in the Johns Hopkins Lung Project

3.5. Bibliographic notes

4. Lead Time Distribution in Cancer Screening

4.1. Introduction

4.2. Lead time distribution when lifetime is fixed

4.2.1. Lead time distribution with two exams

4.2.2. Lead time distribution with any number of exams

4.2.3. Lead time distribution at the j-th exam

4.2.4. Application: The HIP for breast cancer

4.2.5. Application: The Minnesota Colorectal Cancer study

4.3. Lead time distribution when lifetime is random

4.3.1. Probability formulae

4.3.2. The conditional lifetime distribution

4.3.3. Application: The HIP for breast cancer

4.4. Lead time distribution for people with a screening history

4.4.1. Lead time distribution when K1 = K =1

4.4.2. Lead time distribution for any K1 and K

Notes:

Includes bibliographical references and index.

Electronic reproduction. London Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on May 01, 2024).

Local Notes:

Acquired for the Penn Libraries with assistance from the Rosengarten Family Fund.

Other Format:

Print version: Wu, Dongfeng Probability modeling and statistical inference in cancer screening

ISBN:

9781003404125

100340412X

9781003844952

1003844952

Publisher Number:

99996325392

Access Restriction:

Restricted for use by site license.