Statistics and Its Interface

Volume 7 (2014)

Number 1

We dedicate this special issue to Dr. Gang Zheng, a great colleague and dear friend to many of us.

Developments and challenges in statistical methods in cancer surveillance

Pages: 135 – 151

DOI: https://dx.doi.org/10.4310/SII.2014.v7.n1.a14

Authors

Huann-Sheng Chen (Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.)

Angela B. Mariotto (Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.)

Li Zhu (Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.)

Hyune-Ju Kim (Department of Mathematics, Syracuse University, Syracuse, New York, U.S.A.)

Hyunsoon Cho (Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.; and Division of Cancer Registration and Surveillance, National Cancer Center, Korea)

Eric J. Feuer (Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A.)

Abstract

Cancer surveillance includes the monitoring of population levels and trends in incidence, survival, mortality, and prevalence. In addition, data are collected on the factors that influence these basic statistics across the entire cancer control continuum, such as healthy populations at risk of cancer, new diagnosis of cancer, treatment of cancer, living with cancer, and dying of cancer or other causes. To interpret the cancer statistics that are collected, an entire area of statistical methodology has been developed at the U.S. National Cancer Institute (NCI) and other institutions throughout the world. Most of these developments took place in the last 20 years, and the field is still evolving. In this review, we provide an overview of these methods, including the motivation for their development and how the methods compare with more general mainstream statistical methodology; available software; and relevant literature references.

Keywords

cancer surveillance, joinpoint regression model, delay adjustment model, survival analysis, spatial statistics, incidence, mortality, prevalence

2010 Mathematics Subject Classification

62P10

Full Text (PDF format)

Published 8 April 2014