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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.

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Related Experiment Video

Updated: Jun 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Robust tests for matched case-control genetic association studies.

Yong Zang1, Wing Kam Fung

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.

BMC Genetics
|October 13, 2010
PubMed
Summary
This summary is machine-generated.

New stratified genetic model selection (SGMS) and exclusion (SGME) methods improve disease association studies by controlling for confounding factors. These robust tests offer better power in genetic association analyses, especially in matched case-control designs.

Related Experiment Videos

Last Updated: Jun 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • The Cochran-Armitage trend test (CATT) is powerful for disease association but loses power with unknown genetic models.
  • Genetic model selection (GMS) and exclusion (GME) methods were developed to maintain power across common genetic models.
  • Existing methods like GMS/GME can be affected by confounding factors such as age, gender, and race.

Purpose of the Study:

  • To propose stratified genetic model selection (SGMS) and exclusion (SGME) methods to eliminate confounding factor effects in genetic association studies.
  • To investigate the robustness of SGMS and SGME.
  • To compare SGMS/SGME with other robust tests (MAX3, chi-squared with 2 df) in matched case-control designs.

Main Methods:

  • Stratified analysis comparing Hardy-Weinberg disequilibrium coefficients between cases and controls within subpopulations.
  • Adoption of a matching framework to eliminate confounding effects.
  • Simulation studies to evaluate statistical robustness and compare performance with existing methods.

Main Results:

  • Simulation results indicate that SGMS and SGME are robust and effective in controlling for confounding factors.
  • The choice between proposed tests, MAX3, and chi-squared with 2 df depends on the genetic effect of heterozygous genotypes.
  • The proposed methods demonstrated good performance in a real-world matched pair case-control study of sarcoidosis.

Conclusions:

  • The proposed SGMS and SGME methods offer robust procedures for genetic association studies in the presence of confounding factors.
  • These methods provide a valuable alternative for analyzing matched case-control data.
  • The study highlights the importance of accounting for confounders in genetic association analyses.