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Related Concept Videos

Longitudinal Research02:20

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...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Observational Studies01:11

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 example of...
Statistical Methods for Analyzing Epidemiological Data01:25

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 Analysis01:21

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...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Related Experiment Video

Updated: Jun 23, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Methodological issues in a retrospective cancer incidence study.

Jeanine M Buchanich1, Ada O Youk, Gary M Marsh

  • 1Center for Occupational Biostatistics and Epidemiology, Department of Biostatistics, University of Pittsburgh, Pennsylvania 15261, USA. jeanine@pitt.edu

American Journal of Epidemiology
|May 6, 2009
PubMed
Summary

Conducting occupational cancer incidence studies requires significant time and resources due to complex data access. Despite challenges, matching retrospective cohorts with cancer registries is feasible for valuable insights.

Related Experiment Videos

Last Updated: Jun 23, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Occupational health
  • Epidemiology
  • Cancer research

Background:

  • Assessing cancer incidence in occupational cohorts is crucial for understanding workplace health risks.
  • Jet engine manufacturing workers represent a unique occupational group for epidemiological studies.

Purpose of the Study:

  • To trace the incidence of central nervous system (CNS) cancer in a large occupational cohort of jet engine manufacturing workers.
  • To identify and highlight obstacles encountered during a retrospective cancer incidence study using multiple state cancer registries.

Main Methods:

  • A retrospective cohort study design was employed, involving approximately 224,000 employees from 1976 to 2004.
  • Cohort data was matched with cancer registry data from 24 US states, with a focus on central nervous system cancer cases.
  • Obstacles such as application processes, costs, data access, and registry variations were documented.

Main Results:

  • Matching retrospective cohort data with multiple state cancer registries presented significant challenges, including time investment (approx. 700 hours), complex approval processes, and high costs.
  • Data accessibility varied, with some states restricting individual-level data use for research.
  • Approximately 70% of identified CNS cancer cases were located in the state of the facility.

Conclusions:

  • While conducting retrospective cancer incidence studies using multiple state cancer registries is feasible, researchers must carefully plan for substantial time and financial investments.
  • Obstacles encountered, though significant, are potentially resolvable, suggesting the value of such studies for occupational health research.
  • Careful consideration of logistical challenges and potential data limitations is essential for successful cohort-cancer registry linkage studies.