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Diet and lifestyle factors and lung cancer risk and mortality: Classification and regression tree analysis

Principal Investigator
Name
Qian Wang
Degrees
M.D., M.P.H.
Institution
University Hospitals Seidman Cancer Center
Position Title
Assistant Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-633
Initial CDAS Request Approval
Jun 9, 2020
Title
Diet and lifestyle factors and lung cancer risk and mortality: Classification and regression tree analysis
Summary
In addition to tobacco smoking and occupational exposure to asbestosis, which have been the most important risk factors for lung cancer, there has been evidence suggesting dietary factors may also play a role in the development of lung cancer. For instance, higher consumption of fruits and food containing carotenoids and Vitamin C is associated with a lower risk of lung cancer. Prior epidemiology studies also suggested associations between dietary quality using either dietary score or dietary pattern and lung cancer risk but the results have been inconsistent. Nevertheless, the nature of cancer development is a complex process involving multilevel interactions including diet, lifestyle and demographic factors, BMI, genetic predisposition, and environmental exposure. Thus conventional methods, which assume the individual nutrients or diet pattern as an independent factor have limitations in exploring multilevel interactions. Classification and regression tree analysis has been used previously as a method to investigate the complex interactions on a multilevel base in various diseases. Therefore, we propose a study using PLCO data to explore diet and other potential predictor factors of lung cancer incidence and mortality using classification and regression tree analysis.
Aims

1.To explore a variety of dietary, demographic and lifestyle variables (including race, age, gender, BMI, physical activity, occupation, education, tobacco smoking, alcohol use, NSAID use, and metformin use) and their interactions as predictors of lung cancer incidence using classification and regression tree analysis.
2.To examine which of these correlated risk factors is most important for risk stratification in lung cancer overall and stratified by histology (small cell lung cancer versus non-small lung cancer), and smoking status.
3.To investigate the risk factors (in particular, including hormone use) among females and the multilevel interactions with dietary and other lifestyle variables.
4.To assess the predictor factors of survival among lung cancer patients.
5. To assess prior personal history of cancer and its impact on lung cancer risk and mortality. We will also assess the interactions between personal history of cancer and smoking metrics (detailed smoking history, age at starting, age at stopping, pack years, age since quitting), family history of lung cancer, and other lifestyle factors on lung cancer risks and mortality.

Collaborators

NA

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