Dataset Information

Applying the M ₂ Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies.

ABSTRACT: The performance of the limited-information statistic M ₂ for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M ₂ for specific DCMs rather than general modeling frameworks are needed. This article aims to demonstrate the usefulness of M ₂ in hierarchical diagnostic classification models (HDCMs). The performance of M ₂ in evaluating the fit of HDCMs was investigated in the presence of four types of attribute hierarchies. Two simulation studies were conducted to examine Type I error rates and statistical power of M ₂ under different simulation conditions, respectively. The findings suggest acceptable Type I error rates control of M ₂ as well as high statistical power under the conditions of a Q-matrix misspecification and the DINA model misspecification. The data of Examination for the Certificate of Proficiency in English (ECPE) were used to empirically illustrate the suitability of M ₂ in practice.

SUBMITTER: Chen F

PROVIDER: S-EPMC6189476 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Applying the M 2 Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies.

Chen Fu F Liu Yanlou Y Xin Tao T Cui Ying Y

Frontiers in psychology 20181009

The performance of the limited-information statistic M 2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M 2 for specific DCMs rather than general modeling frameworks are needed. This article aims to demonstrate the usefulness of M 2 in hierarchical diagnostic classification models (HDCMs). The performance of M 2 in e ...[more]

PMID: 30356781

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Project description:BackgroundAs the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.Methodology/principal findingsWe introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into "cases" and "controls", we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (lambda = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.Conclusions/significanceIQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.

Dataset Information

Applying the M ₂ Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies.

Publications

Applying the <i>M</i> <sub><i>2</i></sub> Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies.

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