Exploratory Data Analysis to Derive Research Questions on Lung Cancer Screening and Diagnosis
Principal Investigator
Name
Wolfgang Frimmel
Degrees
Ph.D.
Institution
Johannes Kepler University Linz
Position Title
Associate Professor,
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1416
Initial CDAS Request Approval
Apr 28, 2025
Title
Exploratory Data Analysis to Derive Research Questions on Lung Cancer Screening and Diagnosis
Summary
This project aims to conduct a comprehensive exploratory data analysis (EDA) of the National Lung Screening Trial (NLST) datasets. The purpose is to understand the structure, contents, and relationships across datasets in order to derive a focused and meaningful research question for my Master’s thesis in Economics and Business Analytics at Johannes Kepler University Linz.
I intend to explore associations between participant characteristics, imaging findings (CT and X-ray), diagnostic procedures, and lung cancer outcomes. The analysis will also consider differences across sex, age, smoking history, and ethnicity and examine the sequence and effectiveness of screenings and follow-ups.
The goal is to identify a research question that could support evidence-based insights or hypothesis generation on lung cancer detection, treatment, or disparities in outcomes. For this, access to all structured NLST datasets is requested.
I intend to explore associations between participant characteristics, imaging findings (CT and X-ray), diagnostic procedures, and lung cancer outcomes. The analysis will also consider differences across sex, age, smoking history, and ethnicity and examine the sequence and effectiveness of screenings and follow-ups.
The goal is to identify a research question that could support evidence-based insights or hypothesis generation on lung cancer detection, treatment, or disparities in outcomes. For this, access to all structured NLST datasets is requested.
Aims
1. To explore all relevant NLST datasets to gain a comprehensive understanding of lung cancer screening, diagnosis, and treatment pathways.
2. To examine associations between demographic/risk variables and lung cancer outcomes.
3. To identify differences in screening results and procedures across sex, ethnicity, and other factors.
4. To generate a specific research question based on findings from exploratory analysis.
5. To support a Master's thesis project through data-driven discovery and modeling.
Collaborators
Only me.