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Activity Number:
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535
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #328330
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Title:
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Decomposing Pearson's Chi-Squared Test: a Linear Regression and Departure from Linearity
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Author(s):
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Hung-Chih Ku* and Zhengyang Zhou and Chao Xing
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Companies:
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DePaul University and Southern Methodist University and University of Texas Southwestern Medical Center
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Keywords:
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trend test;
Pearson's chi-squared test;
ordinary least squares;
linear regression
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Abstract:
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In genetic case-control association studies, a standard practice is to perform the Cochran-Armitage (CA) trend test under the assumption of the additive model due to its robustness. In simulations, we could even identify situations it outperformed the analysis model used for data simulation. In this study we analytically reveal the statistical basis that leads to the phenomenon. By elucidating the origin of the CA trend test as a linear regression model, we decompose Pearson chi-squared test statistic into two components - one is the CA trend test statistic that measures the goodness of fit of the linear regression model, the other measures the discrepancy between the data and linear regression model. Under this framework we show the additive coding scheme increases the coefficient of determination of the regression model by increasing the spread of data points. We also obtain the conditions under which the CA trend test statistic equals the MAX statistic and Pearson's chi-squared test statistic.
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Authors who are presenting talks have a * after their name.
