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Related Concept Videos

Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Cross-Sectional Research01:50

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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:
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

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Related Experiment Video

Updated: Jun 11, 2026

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

Published on: December 9, 2015

A multiphase method for estimating cohort effects in age-period contingency table data.

Katherine M Keyes1, Guohua Li

  • 1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. kmk2104@columbia.edu

Annals of Epidemiology
|July 15, 2010
PubMed
Summary
This summary is machine-generated.

Men born after 1960 experienced significantly higher homicide rates. This study introduces a three-phase method to analyze age, period, and cohort effects on mortality, revealing increased risks for later birth cohorts.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Understanding demographic influences on disease morbidity and mortality is crucial for identifying etiological factors and developing effective prevention strategies.
  • Age, period, and cohort (APC) effects are key demographic factors influencing health outcomes, but their complex interplay requires robust analytical methods.
  • Traditional APC analyses often treat cohort effects independently, potentially overlooking nuanced interactions with age and period.

Observation:

  • This study analyzed US male homicide mortality data from 1935 to 2004.
  • A novel three-phase method was employed to analyze age, period, and cohort effects.
  • The cohort effect was conceptualized as a partial interaction between age and period.

Findings:

  • A three-phase method was developed, involving graphical inspection, median polish, and residual regression.
  • Individuals born after 1960 showed a significantly elevated homicide rate compared to those born between 1920-1924.
  • Men born between 1980-1984 had over double the homicide rate of men born between 1920-1924 (RR=2.11).

Implications:

  • The proposed three-phase method offers a more interpretable and reliable approach to APC analysis.
  • This method conceptualizes cohort effects as a partial interaction, enhancing etiological understanding.
  • Findings highlight the need for targeted prevention programs addressing cohort-specific risks in homicide mortality.