• Title, Summary, Keyword: Proportional odds model

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Estimation of Odds Ratio in Proportional Odds Model

  • Seo, Min-Ja;Kim, Ju-Sung
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1067-1076
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    • 2006
  • Although the proportional hazards model is the most common approach used for studying the relationship of event times and covariates, alternative models are needed for occasions when it does not fit data. In the two-sample case, proportional odds models are useful for fitting data whose hazard rates converge asymptotically. In this thesis, we propose a new estimator of the relative odds ratio of the proportional odds model when two independent random samples are observed under uncensorship. We prove the asymptotic normality and consistency of the estimator by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

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Estimation in a Two-Sample Proportional Odds Model

  • Kim, Ju-Sung;Seo, Min-Ja
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.327-334
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    • 2005
  • In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model. Also, we show that the estimator is consistent and asymptotically normal. The efficiency of the proposed is assessed through a simulation study.

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Simple Estimation in Proportional Odds Model under Censoring

  • Kim, Ju-Sung;Seo, Min-Ja;Won, Dong-Yu
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.889-898
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    • 2005
  • In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model under censorship. Also, we show that the estimator consistent and asymptotically normal by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

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A Proportional Odds Mixed - Effects Model for Ordinal Data

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.2
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    • pp.471-479
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    • 2007
  • This paper discusses about how to build up mixed-effects model for analysing ordinal response data by using cumulative logits. Random factors are assumed to be coming from the designed sampling scheme for choosing observational units. Since the observed responses of individuals are ordinal, a proportional odds model with two random effects is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

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Goodness-of-fit tests for a proportional odds model

  • Lee, Hyun Yung
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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    • pp.1465-1475
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    • 2013
  • The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

Application of Proportional Odds Models to the Effects of Removing Dental Plaque in Use of Proxabrush (치간칫솔 사용에 따른 치면세균막 제거효과에 대한 비례오즈모형(proportional odds models) 적용)

  • Kim, Jin-Soo;Kim, Jee-Yun;Jorn, Hong-Suk
    • Journal of dental hygiene science
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    • v.8 no.3
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    • pp.169-173
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    • 2008
  • As a result of analyzing the effects of removing dental plaque according to using proxabrush by using the proportional odds models, targeting patients of practicing oral prophylaxis in juniors for the Department of Dental Hygiene at S university from March 10, 2007 to June 3, 2007, the following conclusions were obtained. 1. The goodness-of-fit in the proportional odds models is 1.2552 whose degree of freedom is 3, and p value is .7398, thereby implying that the proportional odds models are appropriate. And, regarding the effects of removing dental plaque and the independent matter of using proxabrush, as the test on $H_0:{\beta}=0$, the test statistics is 15.5496 whose degree of freedom is 1, and p value is 15.5496. This implies that there is high correlation between the effect of removing dental plaque and the use of proxabrush. 2. ML estimate on $\beta$ in the model can be $\hat{\beta}=1.2493$ (ASE = 0.3207). And, as for the tendency that the response will belong to being very good(this can be expressed to be $Y{\leq}j$) rather than being very bad, the tendency of using proxabrush is higher by the estimated odds ratio exp(1.2493) = 3.49 times than the response of not using proxabrush. 3. As for the estimated response in the proportional odds models, the estimated(cumulative) probability, which the response of using proxabrush is very good and will belong to the good effect of removing dental plaque, is 0.38(0.50).

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Modeling of Breast Cancer Prognostic Factors Using a Parametric Log-Logistic Model in Fars Province, Southern Iran

  • Zare, Najaf;Doostfatemeh, Marzieh;Rezaianzadeh, Abass
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.4
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    • pp.1533-1537
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    • 2012
  • In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

Estimation methods and interpretation of competing risk regression models (경쟁 위험 회귀 모형의 이해와 추정 방법)

  • Kim, Mijeong
    • The Korean Journal of Applied Statistics
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    • v.29 no.7
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    • pp.1231-1246
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    • 2016
  • Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

Factors Influencing on the Perception of Helpfulness of Marking the Country of Origin in Predicting the Quality and Safety of Pork (돼지고기 원산지 표시의 도움에 대한 지각도에 미치는 영향 요인 평가)

  • Lee, Seong-Hee;Kang, Jong-Heon
    • Culinary science and hospitality research
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    • v.12 no.3
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    • pp.49-60
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    • 2006
  • The purpose of this study was to measure the factors influencing on the perception of helpfulness of marking the country of origin in predicting the quality and safety of pork. A total of 239 questionnaires were completed. A multinomial logit model is specified in order to estimate which factors influence the probability that a consumer perceives the country of origin as helpful in assessing food quality and food safety. The estimations were carried out using the logistic procedure of SAS. The results are as follows. The proportional odds assumptions of models were not violated at p<0.05. The effects of age, income, children, occupation and respondents informed on the importance of the country of origin in pork quality model were statistically significant. The effects of age, children, occupation and trust on the importance of the country of origin in pork safety model were statistically significant. The results from this study could be useful in developing marketing and health promotion strategies as well as government trade policies.

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Factors Influencing the Level of Perceived Helpfulness of Country of Origin in Predicting the Safety of Chicken Meat (닭고기의 안전 예측에서 원산지 표시의 도움에 대한 지각도에 미치는 영향 요인 평가)

  • Kang Jong-Heon;Lee Seong-Hee
    • Journal of the East Asian Society of Dietary Life
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    • v.16 no.4
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    • pp.488-495
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    • 2006
  • The purposes of this study were to measure respondent's demographic characteristics, respondent's attitudes toward chicken meat, and factors influencing the level of perceived helpfulness of country of origin in predicting the safety of chicken. The data was collected through a consumer survey during the March 2006. Two hundred fifty meat consumers living in Suncheon, the eastern part of Chonnam, were randomly selected as respondents. Eleven respondents did not complete the survey instrument, resulting in a final sample size of 239. All estimations were carried out using correlation, logistic procedure of SAS package, and plum procedure of SPSS. The level of perceived helpfulness of country of origin in predicting the safety of chicken meat was significantly correlated with trust, antibiotics and salmonella/bacteria among the attitude variables. The proportional odds assumption of the model was violated at p<0.05. The estimated results of the multinomial logit model indicated that income, single, occupation, and education significantly affected helpful perception over not helpful perception, while gender and occupation significantly affected very helpful perception over not helpful perception in the case of the extended model. These study results from this study could be useful in developing marketing and health promotion strategies, as well as government trade policy.

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