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Showing 4 results for Subject:
Behrooz Kavehie, Soghrat Faghihzadeh, Farzad Eskandari, Anooshiravan Kazemnejad, Tooba Ghazanfari,
Volume 4, Issue 2 (3-2011)
Abstract
Sometimes it is impossible to directly measure the effect of intervention (medicine or therapeutic methods) in medical researches. That is because of high costs, long time, the aggressiveness of therapeutic methods, lack of clinical responses, and etc. In such cases, the effect of intervention on surrogate variables is measured. Many statistical studies have been accomplished for measuring the validity of surrogates and introducing a criterion for testing. The first criterion was established based on hypothesis testing. Other criterions were introduced over time. Then by using the classic methods, the Likelihood Ratio Factor was introduced. After that, the Bayesian Likelihood Ratio Factor developed and published. This article aims to introduce the Bayesian Likelihood Ratio Factor based on time dependent data. The illness under study is lung disease in victims of chemical weapons. The surrogate therapy method uses the forced expiratory volume at fist second.
Mojdeh Esmailzadeh, Farzad Eskandari, Sima Naghizadeh Ardabili,
Volume 5, Issue 2 (2-2012)
Abstract
Forecasting the future status for underlying systems or random process, is one of the most important problems. In such situations, in addition to variables, the parameters may vary during the time and hence, the independence assumption between variables and parameters is broken. For analyzing this systems, usually the dynamic generalized linear models are used based on Markov chain Monte Carlo algorithm. The purpose of this paper is applying the Bayesian dynamic generalized linear models in non-conjugate discrete structures. First, the concepts of dynamic generalized linear models are reviewed. Then, the Bayesian modeling of non-conjugated discrete structures using MCMC algorithm is studied. Finally, using the investigated model the real data set related to the economic activity condition in three provinces of Iran during the years 2006-2008 are analysed.
Ebrahim Khodaie, Roohollah Shojaei,
Volume 6, Issue 1 (8-2012)
Abstract
Sampling weights are calibrated according to the theory of calibration when the sum of population total for auxiliary variables is known. Under known population, totals for auxiliary variables and some conditions Devile and Sarndal showed that generalized regression estimators could approximate calibration estimators and their variances. In this paper, under unknown population totals for auxiliary variables, an estimator for the population total is proposed and its variance is obtained. It is shown that our estimator for the population total is more efficient than the Horvitz-Thompson estimators by theoretically and simulation results.
Mitra Rahimzadeh, Ahmad Reza Baghestani, Behrooz Kavehei,
Volume 7, Issue 1 (9-2013)
Abstract
On Hypergeometric Generalized Negative Binomial Distribution in Promotion Time Cure Model In analysis of survival data if exposes a high percentage of censoring due to termination of the study, whereas the study has lasted long enough, it is preferred to utilize cure models. These models, which are based on the latent variable distribution, has obtained much attention in the last decade. In this paper the Hypergeometric Generalized Negative Binomial distribution of the latent variable is used to model the long time survival data. The new model parameters are estimated in Bayesian approach. This model is applied for a Primary Biliary Cirrhosis clinical trial data and a simulated data set. With respect to DIC, Hypergeometric Generalized Negative Binomial model is a suitable fit to the data.
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