Volume 14, Issue 4 (Vol.14, No.4, 2019)                   irje 2019, 14(4): 395-403 | Back to browse issues page

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Chehrazi M, Omani Samani R, Tehraninejad E, Chehrazi H, Arabipoor A. Comparison of Maximum Likelihood Estimation and Bayesian with Generalized Gibbs Sampling for Ordinal Regression Analysis of Ovarian Hyperstimulation Syndrome . irje. 2019; 14 (4) :395-403
URL: http://irje.tums.ac.ir/article-1-6203-en.html
1- Assistant Professor, Department of Biostatistics and Epidemiology, School of Medicine, Babol University of Medical Sciences, Babol, Iran
2- Assistant Professor, Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran , samani@royaninstitute.org
3- Associate Professor, Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran; Associate Professor, Department of Obstetrics and Gynecology , Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran
4- Assistant Professor, Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
5- Master of Science, Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
Abstract:   (867 Views)
Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data.
 
Methods: This study used the data from 138 patients of a clinical trial phase III to compare the efficacy of intravenous Albumin and Cabergoline in prevention of ovarian hyperstimulation syndrome. The original study was done between 2010 to 2011 in Royan institute. We compared maximum likelihood and Bayesian estimation with generalized Gibbs sampling for an ordinal regression model based on confidence intervals and standard errors. The model were fit through R 3.3.2 software version.
 
Results: Markov Chain Monte Carlo results reduced the standard errors for estimates and consequently, narrower confidence intervals. Autocorrelations for generalized Gibbs sampler reached to zero in compare to standard Gibbs sampler for shorter time.
 
Conclusion: It seems that confidence intervals of an ordinal regression model are shorter for generalized Gibbs sampler in compare to standard Gibbs and maximum likelihood. It suggests doing more studies to warrant the results.
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Type of Study: Research | Subject: General
Received: 2019/04/16 | Accepted: 2019/04/16 | Published: 2019/04/16

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