• Title/Summary/Keyword: incomplete contingency table

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A Bayesian uncertainty analysis for nonignorable nonresponse in two-way contingency table

  • Woo, Namkyo;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1547-1555
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    • 2015
  • We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

Estimating Missing Cells in Contingency Table with IPE (반복비율적합에 의한 다차원 분할표의 결측칸값 추정)

  • 최현집;신상준
    • The Korean Journal of Applied Statistics
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    • v.13 no.1
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    • pp.197-206
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    • 2000
  • For estimating missing cells in contingency table, we suggest an iterative method which extends IPF (Iterative Proportional Fitting) method. The suggested m~thod is not restricted by the number and the location of missing cells, and does not distort the given quasi-independency.

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Sensitivity analysis of missing mechanisms for the 19th Korean presidential election poll survey (19대 대선 여론조사에서 무응답 메카니즘의 민감도 분석)

  • Kim, Seongyong;Kwak, Dongho
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.29-40
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    • 2019
  • Categorical data with non-responses are frequently observed in election poll surveys, and can be represented by incomplete contingency tables. To estimate supporting rates of candidates, the identification of the missing mechanism should be pre-determined because the estimates of non-responses can be changed depending on the assumed missing mechanism. However, it has been shown that it is not possible to identify the missing mechanism when using observed data. To overcome this problem, sensitivity analysis has been suggested. The previously proposed sensitivity analysis can be applicable only to two-way incomplete contingency tables with binary variables. The previous sensitivity analysis is inappropriate to use since more than two of the factors such as region, gender, and age are usually considered in election poll surveys. In this paper, sensitivity analysis suitable to an multi-dimensional incomplete contingency table is devised, and also applied to the 19th Korean presidential election poll survey data. As a result, the intervals of estimates from the sensitivity analysis include actual results as well as estimates from various missing mechanisms. In addition, the properties of the missing mechanism that produce estimates nearest to actual election results are investigated.

Estimation of Log-Odds Ratios for Incomplete $2{\times}2$ Tables with Covariates using FEFI

  • Kang, Shin-Soo;Bae, Je-Min
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.1
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    • pp.185-194
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    • 2007
  • The information of covariates are available to do fully efficient fractional imputation(FEFI). The new method, FEFI with logistic regression is proposed to construct complete contingency tables. Jackknife method is used to get a standard errors of log-odds ratio from the completed table by the new method. Simulation results, when covariates have more information about categorical variables, reveal that the new method provides more efficient estimates of log-odds ratio than either multiple imputation(MI) based on data augmentation or complete case analysis.

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An Australian Retrospective Study to Evaluate the Prognostic Role of p53 and eIF4E Cancer Markers in Patients with Head and Neck Squamous Cell Carcinoma (HNSCC): Study Protocol

  • Singh, Jagtar;Jayaraj, Rama;Baxi, Siddhartha;Mileva, Mariana;Curtin, Justin;Thomas, Mahiban
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.8
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    • pp.4717-4721
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    • 2013
  • Complete surgical resection of the primary tumour is a crucial predictive step for head and neck squamous cell carcinoma (HNSCC), because incomplete resection may lead to increase in the recurrence rate. Molecular cancer markers have been investigated as potential predictors of prognosis marker, to identify patients who are at high risk of local recurrence. This retrospective study aimed to determine the prognostic correlation between p53 and eIF4E expression and clinical characteristics, recurrence and overall survival. Forty eight HNSCC patients were selected between 2006 and 2009 diagnosed at the Royal Darwin Hospital, Darwin, Northern Territory, Australia. Out of 48, only those 24 with negative surgical margins with hematoxylin and eosin (HandE) were chosedn for further analysis. A total of 77 surgical margins were obtained and subsequently analysed by immunohistochemical (IHC) staining with monoclonal p53 and polyclonal eIF4E antibodies. Contingency table and ${\chi}^2$-test were used to investigate the correlation between p53 and eIF4E expression and clinical characteristics, recurrence and overall survival of the HNSCC patients. The follow up period was 74 months (range 1-74 months). The Kaplan-Meier method was used to generate recurrence and survival curves. This is a first retrospective study of Northern Territory patients, including Indigenous and non-Indigenous Australians. Molecular study of surgical margins could help to identify patients with and without clear margins after surgery and help in choice of the most appropriate adjuvant treatment for HNSCC patients.