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Related Concept Videos

Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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:
H0: The two variables (factors)...
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.
Introduction to Test of Independence01:21

Introduction to Test of Independence

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.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

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Missing Data Sensitivity Analyses for Alcohol Research.

Craig K Enders1, Lara A Ray1

  • 1University of California, Los Angeles, Los Angeles, California, USA.

Alcohol, Clinical & Experimental Research
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a practical guide for sensitivity analyses in alcohol research, addressing missing not at random (MNAR) data. It demonstrates how to assess the impact of unobserved drinking behaviors on study outcomes.

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Area of Science:

  • Alcohol research
  • Biostatistics
  • Clinical trials

Background:

  • Nonresponse in alcohol studies is often linked to unobserved drinking patterns (missing not at random).
  • Participants with severe drinking or those improving may be less likely to complete studies.
  • Understanding these missing data processes is crucial for accurate research findings.

Purpose of the Study:

  • To provide a practical, step-by-step guide for conducting sensitivity analyses in alcohol research.
  • To address the need for clear implementation guidance on sophisticated missing data methods.
  • To compare models with different missing data assumptions to assess conclusion stability.

Main Methods:

  • Focus on selection and shared parameter models to account for drinking behavior influencing nonresponse.
  • Researchers compare models with varying assumptions about the missing data process.
  • Sensitivity analyses are valuable as missing data assumptions are untestable.

Main Results:

  • The study provides a practical account of conducting sensitivity analyses with annotated scripts.
  • While missing data assumptions did not significantly impact this specific study's results, they could in other contexts.
  • The approach helps researchers evaluate the robustness of their findings.

Conclusions:

  • The proposed sensitivity analysis strategy is broadly applicable across alcohol research settings.
  • Data-specific characteristics can influence the application of these methods.
  • Recommendations are provided for reporting sensitivity analysis results effectively.