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Evaluating six-step parsimonious quadratic logistic regression for risk prediction

Principal Investigator
Name
Stuart Baker
Degrees
Sc.D.
Institution
National Cancer Institute
Position Title
Mathematical statistician
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-637
Initial CDAS Request Approval
Jun 18, 2020
Title
Evaluating six-step parsimonious quadratic logistic regression for risk prediction
Summary
The goal of the study is to evaluate the six-step parsimonious quadratic logistic regression model for risk prediction (that I developed) using the NLST x-ray arm data as the training dataset, the PLCO lung cancer data as the independent validation dataset, and Yunnan Tin Miners Cohort Study data as the second independent validation dataset. Dr Ping Hu in BRG/DCP/NCI along with programmers from IMS created a NLST and PLCO data set for this type of evaluation and has generously offered to share it with me if I get this approval.
Aims

Fit the risk prediction model to the NLST x-ray arm data and evaluate performance using PLCO lung cancer data and data from Yunnan Tin Miners Cohort Study.

Collaborators

Dr. Ping Hu. Biometry Research Group, National Cancer Institute