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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

Bias correction to secondary trait analysis with case-control design.

Hua Yun Chen¹, Rick Kittles, Wei Zhang

¹Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612 USA. hychen@uic.edu

Statistics in Medicine

|September 19, 2012

Summary

This study introduces a new method to correct for bias in genetic association studies when analyzing secondary traits. The approach accurately assesses genetic marker associations, even with biased sample ascertainment.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Area of Science:

Genetics
Biostatistics
Epidemiology

Background:

Genetic association studies often analyze primary and secondary phenotypes with densely typed genetic markers.
Case-control and extreme-value sampling designs are common for efficient primary phenotype ascertainment.
Secondary trait analysis without accounting for sample ascertainment can lead to biased genetic association estimates.

Purpose of the Study:

To propose a novel statistical method for correcting potential bias in secondary trait genetic association analysis.
To address inadequate adjustment for sample ascertainment in genetic studies.
To provide explicit correction formulas for screening genetic markers and evaluating result sensitivity.

Main Methods:

Developed a new statistical method with explicit correction formulas.
Utilized simulation studies to compare the proposed approach with existing methods (compensator, maximum prospective likelihood).
Applied the method to analyze the genetic association of prostate-specific antigen in a prostate cancer case-control study.

Main Results:

The proposed method effectively corrects for bias arising from sample ascertainment in secondary trait analyses.
Simulation studies showed good performance compared to computationally intensive approaches.
Demonstrated practical application in a real-world genetic association study of prostate cancer.

Conclusions:

The new method offers an efficient and accurate way to analyze secondary traits in genetic association studies with biased sample ascertainment.
The explicit formulas allow for rapid screening of genetic markers and sensitivity analyses.
This approach improves the reliability of genetic association findings, particularly in case-control designs.