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...

Observational Studies

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

You might also read

Related Articles

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

Sort by

Same author

Relationships Between Advance Care Planning Engagement, Patients' Religious Practices, and Spirituality.

The American journal of hospice & palliative care·2026

Same author

Partial Nephrectomy in Solitary Kidneys: Intraoperative Techniques and Their Impact on Chronic Kidney Disease Progression.

Cancers·2026

Same author

A Fracture Healing Odyssey: Kinematic Comparison of Unions and Nonunions in Human Lower-Extremity Long Bones Treated with Intramedullary Nailing: A Retrospective Cohort Study.

The Journal of bone and joint surgery. American volume·2026

Same author

Prognostic Significance and Biomarker Identification by Progression Patterns in Patients With Small Cell Lung Cancer After Immune Checkpoint Inhibitors.

Clinical lung cancer·2026

Same author

Lung T1-MRI and Multi-Breath Washout Detect Longitudinal Changes in Children 6-11 Years Old After Beginning Elexacaftor/Tezacaftor/Ivacaftor (ETI) Therapy.

Pediatric pulmonology·2026

Same author

The Digital Information Environment of Lung Cancer and Lung Cancer Screening: Protocol for a Cross-Platform Social Media Content Analysis.

JMIR research protocols·2026

Search research articles

Related Experiment Video

Updated: Jan 19, 2026

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

Understanding lung cancer screening behaviour using path analysis.

Lisa Carter-Harris¹, James E Slaven², Patrick O Monahan²

¹Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, USA.

Journal of Medical Screening

|September 26, 2019

Summary

Understanding lung cancer screening behavior requires examining individual factors. Key influences include clinician recommendations, self-efficacy, and reduced mistrust, which are crucial for effective lung cancer screening interventions.

Keywords:

Lung cancer screening conceptual model health behaviour health beliefs path analysis

More Related Videos

Lung Tumor Cell Recruitment Assay

Lung Tumor Cell Recruitment Assay

Published on: February 26, 2019

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Published on: March 12, 2018

Related Experiment Videos

Last Updated: Jan 19, 2026

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

Lung Tumor Cell Recruitment Assay

Lung Tumor Cell Recruitment Assay

Published on: February 26, 2019

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Practical Considerations in Studying Metastatic Lung Colonization in Osteosarcoma Using the Pulmonary Metastasis Assay

Published on: March 12, 2018

Area of Science:

Medical research
Public health
Behavioral science

Background:

Understanding lung cancer screening behavior is vital for developing interventions.
Previous qualitative studies explored attitudes, but factors influencing screening decisions remain unclear.
This study addresses the gap by testing a theoretical framework for lung cancer screening participation.

Purpose of the Study:

To test an explanatory framework for lung cancer screening participation from the individual's perspective.
To identify theoretically grounded factors influencing screening-eligible individuals.
To provide insights for designing targeted interventions.

Main Methods:

A national purposive sample of 515 screening-eligible participants in the United States was recruited.
Data were collected via surveys assessing constructs of the Conceptual Model for Lung Cancer Screening Participation.
Path analysis was used to examine relationships between variables.

Main Results:

Clinician recommendation, higher self-efficacy, and lower mistrust were directly associated with screening participation.
Self-efficacy's link to screening behavior was mediated by fatalism, fear, family history, knowledge, income, clinician recommendation, and social influence.
Medical mistrust, self-efficacy, and clinician recommendation significantly impacted lung cancer screening decisions.

Conclusions:

Medical mistrust, self-efficacy, and clinician recommendation are significant factors in lung cancer screening decisions.
Findings highlight potentially modifiable targets for intervention.
This understanding is critical for designing effective clinician- and patient-focused interventions.