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- 저자 : Baghfalaki, Taban (검색결과 4 건)
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T. Baghfalaki,M. Ganjali,D. Berridge 한국통계학회 2018 Journal of the Korean Statistical Society Vol.47 No.4
Time-course gene sets are collections of predefined groups of genes in some patients gathered over time. The analysis of time-course gene sets for testing gene sets which vary significantly over time is an important context in genomic data analysis. In this paper, the method of generalized estimating equations (GEEs), which is a semi-parametric approach, is applied to time-course gene set data. We propose a special structure of working correlation matrix to handle the association among repeated measurements of each patient over time. Also, the proposed working correlation matrix permits estimation of the effects of the same gene among different patients. The proposed approach is applied to an HIV therapeutic vaccine trial (DALIA-1 trial). This data set has two phases: pre-ATI and post-ATI which depend on a vaccination period. Using multiple testing, the significant gene sets in the pre-ATI phase are detected and data on two randomly selected gene sets in the post-ATI phase are also analyzed. Some simulation studies are performed to illustrate the proposed approaches. The results of the simulation studies confirm the good performance of our proposed approach.
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A linear mixed model for analyzing longitudinal skew-normal responses with random dropout
M. Ganjali,T. Baghfalaki,M. Khazaei 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.2
In this paper, a linear mixed effects model is used to fit skewed longitudinal data in the presence of dropout. Two distributional assumptions are considered to produce background for heavy tailed models. One is the linear mixed model with skew-normal random effects and normal errors and the other one is the linear mixed model with skewnormal errors and normal random effects. An ECM algorithm is developed to obtain the parameter estimates. Also an empirical Bayes approach is used for estimating random effects. A simulation study is implemented to investigate the performance of the presented algorithm. Results of an application are also reported where standard errors of estimates are calculated using the Bootstrap approach.
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Moradzadeh, Rahmatollah,Mansournia, Mohammad Ali,Baghfalaki, Taban,Ghiasvand, Reza,Noori-Daloii, Mohammad Reza,Holakouie-Naieni, Kourosh Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.18
Background: Misreporting self-reported family history may lead to biased estimations. We used Bayesian methods to adjust for exposure misclassification. Materials and Methods: A hospital-based case-control study was used to identify breast cancer risk factors among Iranian women. Three models were jointly considered; an outcome, an exposure and a measurement model. All models were fitted using Bayesian methods, run to achieve convergence. Results: Bayesian analysis in the model without misclassification showed that the odds ratios for the relationship between breast cancer and a family history in different prior distributions were 2.98 (95% CRI: 2.41, 3.71), 2.57 (95% CRI: 1.95, 3.41) and 2.53 (95% CRI: 1.93, 3.31). In the misclassified model, adjusted odds ratios for misclassification in the different situations were 2.64 (95% CRI: 2.02, 3.47), 2.64 (95% CRI: 2.02, 3.46), 1.60 (95% CRI: 1.07, 2.38), 1.61 (95% CRI: 1.07, 2.40), 1.57 (95% CRI: 1.05, 2.35), 1.58 (95% CRI: 1.06, 2.34) and 1.57 (95% CRI: 1.06, 2.33). Conclusions: It was concluded that self-reported family history may be misclassified in different scenarios. Due to the lack of validation studies in Iran, more attention to this matter in future research is suggested, especially while obtaining results in accordance with sensitivity and specificity values.
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Bayesian testing of agreement criteria under order constraints
M. Ganjali,N. Moradzadeh,T. Baghfalaki 한국통계학회 2017 Journal of the Korean Statistical Society Vol.46 No.1
The most popular criterion to measure the overall agreement between two raters is the Cohen’s kappa coefficient. This coefficient measures the agreement of two raters who judge about some subjects with a binary nominal rating. In this paper, we consider a unified Bayesian approach for testing some hypotheses about the kappa coefficients under order constraints. This is done for rating of more than two studies with binary response. The Monte Carlo Markov Chain (MCMC) approach is used for the model implementation. The approach is illustrated using some simulation studies. Also, the proposed method is applied for analyzing a real data set.
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