Skip to Main Content
An official website of the United States government

Prostate Cancer Survival in PLCO: The effect of screening

Principal Investigator

Name
Paul Pinsky

Institution
NCI, DCP, EDRG

Email
pinskyp@mail.nih.gov

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
2010-0301

Initial CDAS Request Approval
Mar 3, 2011

Title
Prostate Cancer Survival in PLCO: The effect of screening

Summary
Melanoma incidence continues to increase worldwide. Since 1973, the US melanoma incidence rate has tripled. The US has also been plagued by an obesity epidemic, with rates in the US increasing dramatically during a similar time period as melanoma. Epidemiological studies have suggested that there may be a correlation between obesity and melanoma. Obesity has been significantly associated with as much as a 2.5 fold increase in melanoma risk. While it is unclear what biologic mechanisms or behavioral factors may mediate the relationship of obesity, body mass index (BMI), and melanoma, it has been suggested that obesity reduction may translate to benefits in the prevention of melanoma. With looming public health implications, epidemiologic investigation of an association of melanoma with obesity is imperative in the battle against both of these epidemics. However it is unclear if biologic or behavioral factors mediate the relationship of BMI and melanoma and there has been a paucity of information on women to study. The objective of our study is to examine the association of melanoma with BMI among participants in the Prostate, Lung, Colorectal & Ovarian (PLCO) Cancer Screening Trial.

Aims

Aim 1. To compute prostate-specific (and overall) survival rates in PLCO by various screening-related factors, including study arm, method of diagnosis and time period (study year). Aim 2. To compute expected prostate-specific (and overall) survival rates in PLCO based on the years of diagnosis and age of cases, utilizing SEER national statistics. Aim 3. To compute observed and expected survival rates by Gleason categories, as well as to calculate observed and expected incidence by Gleason categories. Aim 4. To analyze over diagnosis, lead time and survival rates, overall and by Gleason category, to understand the relationship between them.

Collaborators

Gerald Andriole (Washington UNiv)
Amanda Black (DCEG)
RObert Grubb (Washington Univ)

Related Publications