Dongfeng Wu

Probability Modeling and Statistical Inference in Cancer Screening (eBook, PDF)

• Format: PDF

Produktbeschreibung

There are many unsolved problems in the study of cancer screening: how to estimate key parameters involved in screening exams, such as screening sensitivity; how to estimate distribution of lead time; how to evaluate the long-term effects of screening, including probability of over diagnosis, true-early-detection and no-early-detection, etc.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 286
• Erscheinungstermin: 6. Februar 2024
• Englisch
• ISBN-13: 9781003844945
• Artikelnr.: 69629532

Autorenporträt

Dongfeng Wu is a full professor in the Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville. She got her PhD in Statistics and MS in Computer Science from the University of California, Santa Barbara in 1999. She worked at Mississippi State University and MD Anderson Cancer Center before joining the University of Louisville.

Inhaltsangabe

1. A Brief Review Of Probability and Bayes Inference. 2. Estimating The Three Key Parameters 3. Testing Dependency Of Two Screening Modalities. 4. Lead Time Distribution in Cancer Screening. 5. Evaluating Long Term Outcomes in Screening. 6. Scheduling Screening Exam for Asyptomatic Individuals. 7. Scheduling the Upcoming Exam for Individuals with a Screening History.

Rezensionen

"This book describes statistical methods for analyzing cancer screening data, focusing on cancers that can be modeled as a progressive three-state process: cancer-free, preclinical (asymptomatic) cancer, and clinical (symptomatic) cancer. The goal is to estimate key parameters of interest to cancer screening programs, such as per-screen sensitivity for cancer, time spent cancer-free, sojourn time (i.e., time spent in the preclinical cancer state), lead time (i.e., the difference in cancer diagnosis time with vs. without screening), and the percentage of positive screens that are over-diagnosed. The material is presented for students engaged in a graduate-level statistics course on the topic, with the only prerequisite knowledge being calculus, probability, and introductory statistical inference."
~Li C. Cheung (26 Nov 2024), Journal of the American Statistical Association