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

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Published on: June 21, 2018

Two-stage group sequential robust tests in family-based association studies: controlling type I error.

Lihan K Yan¹, Gang Zheng, Zhaohai Li

¹Biostatistics Program, Department of Statistics, George Washington University, Washington DC 20052, USA.

Annals of Human Genetics

|March 8, 2008

Summary

This study introduces a robust statistical method for family-based genetic association studies when disease models are unknown. It proposes type I error control for group sequential analysis using a maximum test, enhancing power and reliability.

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Area of Science:

Genetics
Biostatistics
Epidemiology

Background:

Family-based association studies require specific genetic models (e.g., recessive, additive) for optimal statistical testing.
Unknown genetic models for complex diseases can lead to reduced statistical power if a single, mis-specified model is used.
Robust statistical tests, like the maximum of several model-specific tests, are preferred when genetic models are uncertain.

Purpose of the Study:

To propose and compare methods for controlling type I error rates in family-based candidate-gene association studies using a group sequential approach with a maximum test.
To provide critical values for a two-stage group sequential robust procedure with a single interim analysis.

Main Methods:

The study focuses on a robust test statistic, which is the maximum of several tests, each optimal for a specific genetic model.
It introduces group sequential analysis, allowing interim analyses to increase efficiency.
Methods for controlling the overall type I error rate are developed and compared for this specific application.

Main Results:

The proposed group sequential approach with a maximum test is designed to be robust against genetic model mis-specification.
For a two-stage design with one interim analysis, specific critical values are derived using alpha spending functions.
These critical values control the overall type I error rate for the robust group sequential procedure.

Conclusions:

The developed group sequential robust procedure is suitable for family-based candidate-gene association studies with unknown genetic models.
This approach offers a statistically sound method for interim analyses while maintaining overall error rate control.
The provision of critical values facilitates the practical application of this robust methodology in genetic research.