Showing 2 results for Maximum Likelihood
Ar Soltanian, S Faghihzadeh, A Gerami, D Mehdibarzi, J Jing Cheng,
Volume 6, Issue 1 (6-2010)
Abstract
Background & Objectives: In clinical trials some of participants do not take assignment treatment. Intention-to-treat (ITT) is one of the strategies to analyze of clinical trials with control. ITT estimation will be invalid and incorrect to show of treatment effects in case of existing non-compliance in participants. In this study we adjusted noncompliance effect to compare of active treatment and placebo.
Methods: To demonstrate efficiency of proposed model, a dataset of crossover clinical trial with 42 patients with knee osteoarthritis was used. To estimate the non-compliance’s effect adjusting at comparison of treatment effects, we use mean of compliance proportion at periods in sequences. The parameters were estimated by maximum likelihood method. ( could you ask authors to have a look at what they wrote and compare with Farsi version)
Results: The results show that baseline variables distributions like duration of disease, severity of disease, age and sex, were not significant (p>0.05). The standard error estimation of treatment effects ( ) based on adjusted model were less than standard model (0.09 and 0.12, respectively). In addition, likelihood ratio statistics based on adjusted model were less than standard model (1177.7 versus 1205.1).
Conclusion: Based on estimation of standard errors and likelihood ratio statistics at adjusted and standard models, we observe that adjusted model is more efficient than standard model.
Y Mehrabi, E Maraghi, H Alavi Majd, Me Motlagh,
Volume 6, Issue 3 (12-2010)
Abstract
Background and objective: Disease or mortality mapping are statistical methods aimed at providing precise estimates of rates across geographical maps. The aim of this research is to improve the precision of relative risk (RR) estimates of infant mortality (IM) for different rural areas, using empirical and full Bayesian methods.
Methods: Infant mortality data were extracted from the vital horoscope (Zij-Hayati) for years 2001 and 2006 across rural areas of Iran. Maximum Likelihood, Empirical Bayes with Poisson-Gamma model and full Bayesian models were used. Mont Carlo Markov Chain method was used for latter models. Deviance information criterion (DIC) was computed to check the models fittings. R, WinBUGS and Arc GIS software were employed.
Results: Based on the full Bayesian method, the highest RR of infant mortality was 1.73 (95%CI: 1.58-1.88) in year 2001 and 1.62 (95%CI: 1.50-1.75) in 2006 which belonged to Sistan-va-Blouchestan area in comparison to the whole country. In 2001, the rural areas of Birjand (1.45), Kordistan (1.23) and Khorasan (1.21) and in 2006, Birjand (1.42), Zanjan (1.39), Kordistan (1.36), Ardebil (1.32), Zabol (1.28), West Azerbaijan (1.18) and finally Golestan (1.14) had significant RR of IM (all p<0.05). The lowest RR of infant mortality for year 2001 were belong to rural areas of Tehran University (0.56) and for year 2006 to former Iran University (0.52).
Conclusion: To estimate the mortality map parameters, the full Bayesian method is preferred compared to empirical Bayes and maximum likelihood.
