Shyh-Huei Chen

Publications (7)20.42 Total impact

• Article: Psychometric properties of a computerized adaptive test for assessing mobility in older adults using novel video-animation technology.

  Edward H Ip, W Jack Rejeski, Anthony P Marsh, Ryan Barnard, Shyh-Huei Chen
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  ABSTRACT: PURPOSE: This paper reports on the psychometric properties of a computerized adaptive test (CAT) version of the Mobility Assessment Tool (MAT) for older adults (MAT-CAT). METHODS: An item pool of 78 video-animation-based items for mobility was developed, and response data were collected from a sample of 234 participants aged 65-90 years. The video-animation-based instrument was designed to minimize ambiguity in the presentation of task demands. In addition to evaluating traditional psychometric properties including dimensionality, differential item functioning (DIF), and local dependence, we extensively tested the performance of several MAT-CAT measures and compared their performances with a fixed format. RESULTS: Operationally, the MAT-CAT was sufficiently unidimensional and had acceptable levels of local independence. One DIF item was removed. Most importantly, the CAT measures showed that even starting with a single fixed item at the mean ability, the adaptive version delivered better performance than the fixed format in terms of several criteria including the standard error of estimate. CONCLUSION: The MAT-CAT demonstrated satisfactory psychometric properties and superior performance to a fixed format. The video-animation-based adaptive instrument can be used for assessing mobility with specificity and precision.
  Quality of Life Research 01/2013; · 2.30 Impact Factor
• Article: Using graded response model for the prediction of prostate cancer risk.

  Shyh-Huei Chen, Edward H Ip, Jianfeng Xu, Jielin Sun, Fang-Chi Hsu
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  ABSTRACT: Disease risk-associated single nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWAS) have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. To date, a stable summary estimate of the joint effect of genetic variants on disease risk prediction is not available. In this study, we propose to use the graded response model (GRM), which is based on the item response theory, for estimating the individual risk that is associated with a set of SNPs. We compare the GRM with a recently proposed risk prediction model called cumulative relative risk (CRR). Thirty-three prostate cancer risk-associated SNPs were originally discovered in GWAS by December 2009. These SNPs were used to evaluate the performance of GRM and CRR for predicting prostate cancer risk in three GWAS populations, including populations from Sweden, Johns Hopkins Hospital, and the National Cancer Institute Cancer Genetic Markers of Susceptibility study. Computational results show that the risk prediction estimates of GRM, compared to CRR, are less biased and more stable.
  Human Genetics 03/2012; 131(8):1327-36. · 5.07 Impact Factor
• Article: Genome-wide two-locus epistasis scans in prostate cancer using two European populations.

  Sha Tao, Junjie Feng, Timothy Webster, Guangfu Jin, Fang-Chi Hsu, Shyh-Huei Chen, Seong-Tae Kim, Zhong Wang, Zheng Zhang, Siqun L Zheng, William B Isaacs, Jianfeng Xu, Jielin Sun
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  ABSTRACT: Approximately 40 single nucleotide polymorphisms (SNPs) that are associated with prostate cancer (PCa) risk have been identified through genome-wide association studies (GWAS). However, these GWAS-identified PCa risk-associated SNPs can explain only a small proportion of heritability (~13%) of PCa risk. Gene-gene interaction is speculated to be one of the major factors contributing to the so-called missing heritability. To evaluate the gene-gene interaction and PCa risk, we performed a two-stage genome-wide gene-gene interaction scan using a novel statistical approach named "Boolean Operation-based Screening and Testing". In the first stage, we exhaustively evaluated all pairs of SNP-SNP interactions for ~500,000 SNPs in 1,176 PCa cases and 1,101 control subjects from the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) study. No SNP-SNP interaction reached a genome-wide significant level of 4.4E-13. The second stage of the study involved evaluation of the top 1,325 pairs of SNP-SNP interactions (P(interaction) <1.0E-08) implicated in CGEMS in another GWAS population of 1,964 PCa cases from the Johns Hopkins Hospital (JHH) and 3,172 control subjects from the Illumina iControl database. Sixteen pairs of SNP-SNP interactions were significant in the JHH population at a P(interaction) cutoff of 0.01. However, none of the 16 pairs of SNP-SNP interactions were significant after adjusting for multiple tests. The current study represents one of the first attempts to explore the high-dimensional etiology of PCa on a genome-wide scale. Our results suggested a list of SNP-SNP interactions that can be followed in other replication studies.
  Human Genetics 02/2012; 131(7):1225-34. · 5.07 Impact Factor
• Article: Gibbs Ensembles for Nearly Compatible and Incompatible Conditional Models.

  Shyh-Huei Chen, Edward H Ip, Yuchung J Wang
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  ABSTRACT: Gibbs sampler has been used exclusively for compatible conditionals that converge to a unique invariant joint distribution. However, conditional models are not always compatible. In this paper, a Gibbs sampling-based approach - Gibbs ensemble -is proposed to search for a joint distribution that deviates least from a prescribed set of conditional distributions. The algorithm can be easily scalable such that it can handle large data sets of high dimensionality. Using simulated data, we show that the proposed approach provides joint distributions that are less discrepant from the incompatible conditionals than those obtained by other methods discussed in the literature. The ensemble approach is also applied to a data set regarding geno-polymorphism and response to chemotherapy in patients with metastatic colorectal.
  Computational Statistics & Data Analysis 04/2011; 55(4):1760-1769. · 1.03 Impact Factor
• Article: Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture.

  Chen-Chiang Lin, Yang-Kun Ou, Shyh-Huei Chen, Yung-Ching Liu, Jinn Lin
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  ABSTRACT: Older patients with hip fracture have a mortality rate one year after surgery of 20-30%. The purpose of this study is to establish a predictive model to assess the outcome of surgical treatment in older patients with hip fracture. A database of information from 286 consecutive cases of surgery for hip fracture from the Department of Orthopedics, National Taiwan University Hospital Yun-Lin Branch, was utilised for model building and testing. Both logistic regression and artificial neural network (ANN) models were developed. Cases were randomly assigned to training and testing datasets. A testing dataset was utilised to test the accuracy of both models (n=89). The areas under the receiver operator characteristic curves of both models were utilised to compare predictability and accuracy. The logistic regression training and testing datasets had an area of 0.938 (95% CI: 0.904, 0.972) and 0.784 (95% CI: 0.669, 0.899), respectively, below the 0.998 (95% CI: 0.995, 1.000) and 0.949 (95% CI: 0.857, 1.000) of the final ANN model. Overall, ANNs have higher predictive ability than logistic regression, perhaps because they are not affected by interactions between factors. They may assist in complex decision making in the clinical setting.
  Injury 08/2010; 41(8):869-73. · 1.98 Impact Factor
• Article: A multigenic approach to evaluating prostate cancer risk in a systematic replication study.

  Fang-Chi Hsu, Sara Lindström, Jielin Sun, Fredrik Wiklund, Shyh-Huei Chen, Hans-Olov Adami, Aubrey R Turner, Wennuan Liu, Katarina Bälter, Jin Woo Kim, Pär Stattin, Bao-Li Chang, William B Isaacs, Jianfeng Xu, Henrik Grönberg, S Lilly Zheng
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  ABSTRACT: Although it is well known that multiple genes may influence prostate cancer risk, most current efforts at identifying prostate cancer risk variants rely on single-gene approaches. In previous work using mostly single-gene approaches, we observed significant associations (P < 0.05) for 6 of 46 polymorphisms in five genes in a Swedish prostate cancer case-control study population. We now report on the higher-order gene-gene interactions among those 46 genetic variants and the combined effect of the six polymorphisms with significant main effects for association with prostate cancer risk in 795 controls and 1,461 cases. Classification and regression tree analysis was used to evaluate higher-order gene-gene interactions. No interactions were confirmed by the result from logistic regressions. For the combined analysis, we tested the hypothesis that individuals carrying multiple copies of risk variants are at increased risk for prostate cancer. Individuals carrying more than eight copies of any risk variant were almost twofold more likely to get prostate cancer (OR = 1.99, P = 0.0014). A significant trend relationship was observed (P < 0.0001). In the present study, additive effects but not multiplicative effects among these six polymorphisms with significant main effects were observed.
  Cancer genetics and cytogenetics 06/2008; 183(2):94-8. · 1.54 Impact Factor
• Article: A support vector machine approach for detecting gene-gene interaction.

  Shyh-Huei Chen, Jielin Sun, Latchezar Dimitrov, Aubrey R Turner, Tamara S Adams, Deborah A Meyers, Bao-Li Chang, S Lilly Zheng, Henrik Grönberg, Jianfeng Xu, Fang-Chi Hsu
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  ABSTRACT: Although genetic factors play an important role in most human diseases, multiple genes or genes and environmental factors may influence individual risk. In order to understand the underlying biological mechanisms of complex diseases, it is important to understand the complex relationships that control the process. In this paper, we consider different perspectives, from each optimization, complexity analysis, and algorithmic design, which allows us to describe a reasonable and applicable computational framework for detecting gene-gene interactions. Accordingly, support vector machine and combinatorial optimization techniques (local search and genetic algorithm) were tailored to fit within this framework. Although the proposed approach is computationally expensive, our results indicate this is a promising tool for the identification and characterization of high order gene-gene and gene-environment interactions. We have demonstrated several advantages of this method, including the strong power for classification, less concern for overfitting, and the ability to handle unbalanced data and achieve more stable models. We would like to make the support vector machine and combinatorial optimization techniques more accessible to genetic epidemiologists, and to promote the use and extension of these powerful approaches.
  Genetic Epidemiology 03/2008; 32(2):152-67. · 3.44 Impact Factor