Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Eligibility and Prognostic Performance of Smoking Duration-Based Versus Pack-Year-Based U.S. National Lung Cancer Screening Criteria Across Racial and Ethnic Groups.

Annals of internal medicine·2025

Same author

Racial and ethnic differences in second primary lung cancer risk among lung cancer survivors.

JNCI cancer spectrum·2024

Same author

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice.

Journal of computer assisted tomography·2024

Same author

Chronic Chest Computed Tomography Findings Following COVID-19 Pneumonia.

Seminars in ultrasound, CT, and MR·2024

Same author

Second Primary Lung Cancer Among Lung Cancer Survivors Who Never Smoked.

JAMA network open·2023

Same author

Risk model-based management for second primary lung cancer among lung cancer survivors through a validated risk prediction model.

Cancer·2023

Same journal

Circulating Tumor DNA Status and Adjuvant Chemotherapy in Resected Colorectal Liver Metastases.

JAMA oncology·2026

Same journal

Cardiac Risk After Heart-Sparing Breast Radiotherapy.

JAMA oncology·2026

Same journal

When Not to Push the Boulder.

JAMA oncology·2026

Same journal

Circulating Methylated SEPT9 for Detection of Hepatocellular Carcinoma in Cirrhosis.

JAMA oncology·2026

Same journal

Less Can Be More by Circulating Tumor DNA Use in ERBB2-Positive Breast Cancer.

JAMA oncology·2026

Same journal

Long-Term Outcomes in Patients With Recurrent Ovarian Cancer and Exceptional Response to PARP Inhibitors.

JAMA oncology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 12, 2025

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Risk Model-Based Lung Cancer Screening and Racial and Ethnic Disparities in the US.

Eunji Choi^1,2, Victoria Y Ding¹, Sophia J Luo¹

¹Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California.

|October 26, 2023

Summary

This summary is machine-generated.

Risk-based lung cancer screening significantly reduces racial disparities and improves screening performance compared to current USPSTF 2021 guidelines. This approach enhances lung cancer detection across diverse ethnic groups.

More Related Videos

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Published on: March 17, 2020

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Published on: February 27, 2020

Related Experiment Videos

Last Updated: Jul 12, 2025

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Published on: March 17, 2020

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

Published on: February 27, 2020

Area of Science:

Oncology
Public Health
Epidemiology

Background:

The 2021 US Preventive Services Task Force (USPSTF) guidelines for lung cancer screening reduced disparities for African American vs. White individuals but did not address other groups.
Risk model-based screening may mitigate disparities and improve lung cancer screening performance, yet validation across diverse populations is lacking.

Purpose of the Study:

To validate and recalibrate the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCOm2012) model across U.S. racial and ethnic groups.
To evaluate disparities and screening performance using risk-based screening with the PLCOm2012 model versus the USPSTF 2021 criteria.

Main Methods:

Utilized the Multiethnic Cohort Study (105,261 participants) with data from 1993-2018.
Calculated 6-year lung cancer risk using a recalibrated PLCOm2012 model (PLCOm2012-Update) and assessed screening eligibility (≥1.3% risk).
Compared screening performance (eligibility-incidence ratio, sensitivity, specificity, number needed to screen) between risk-based screening and USPSTF 2021 criteria across racial/ethnic groups.

Main Results:

The PLCOm2012-Update demonstrated good predictive accuracy (AUC, 0.72-0.82) across races and ethnicities.
USPSTF 2021 criteria showed significant disparities, with a 53% lower eligibility-incidence ratio for African Americans vs. White individuals.
Risk-based screening substantially reduced this disparity (E-I ratio: 15.9 vs. 18.4) and showed minimal disparities in other minority groups.
Risk-based screening improved overall sensitivity (67.2% vs. 57.7%) and reduced the number needed to screen (26 vs. 30) compared to USPSTF 2021 criteria, at similar specificity (76.6%).

Conclusions:

Risk-based lung cancer screening effectively reduces racial and ethnic disparities.
This approach improves lung cancer screening performance across diverse populations compared to the USPSTF 2021 criteria.