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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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Related Experiment Video

Updated: Jul 4, 2025

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication
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Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification

Pooneh Malekifar1, Saharnaz Nedjat1, Ibrahim Abdollahpour2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Archives of Iranian Medicine
|February 4, 2024
PubMed
Summary

This study reveals that alcohol consumption increases the risk of developing multiple sclerosis (MS). Adjusting for biases confirms a positive causal link between drinking and MS incidence.

Keywords:
Alcohol consumptionG-formulaModel-based standardizationMonte Carlo sensitivity analysisMultiple sclerosisProbabilistic bias analysis

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Area of Science:

  • Epidemiology
  • Neuroscience
  • Public Health

Background:

  • The precise causes of multiple sclerosis (MS) remain unclear.
  • Assessing risk factors like alcohol consumption is complicated by confounding and measurement bias.

Purpose of the Study:

  • To determine the causal effect of alcohol consumption on MS.
  • To adjust for misclassification bias in self-reported alcohol intake and confounding factors.

Main Methods:

  • Population-based incident case-control study with 547 MS patients and 1057 controls.
  • Causal directed acyclic graph used to identify confounders.
  • Probabilistic bias analysis method (PBAM) and model-based standardization (MBS) applied to adjust for bias and estimate causal effects.

Main Results:

  • Adjusted risk ratio for alcohol consumption and MS ranged from 1.93 to 2.02.
  • Population attributable fraction (PAF) was between 0.15 and 0.17.
  • Risk difference was 0.0001.

Conclusions:

  • Alcohol consumption demonstrates a positive causal effect on the incidence of multiple sclerosis.
  • Findings adjusted for measurement bias, confounding, and random error.