Syndrome of congenital cataracts, sensorineural deafness, Down syndrome-like facial appearance, short stature, and mental retardation: Two additional cases

2007 ◽  
Vol 143A (21) ◽  
pp. 2581-2587 ◽  
Author(s):  
Kim Keppler-Noreuil ◽  
Judy Welch ◽  
Katherine Baker-Lange
Keyword(s):  
Down Syndrome ◽  
Mental Retardation ◽  
Short Stature ◽  
Sensorineural Deafness ◽  
Facial Appearance ◽  
Congenital Cataracts

scholarly journals Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Reem Aljarallah ◽  
Samer A Kharroubi
Keyword(s):  
Monte Carlo ◽  
Down Syndrome ◽  
Mental Retardation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Univariate Analysis ◽  
Deviance Information Criterion ◽  
Predictive Ability ◽  
Information Criterion ◽  
Medical Genetic

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

Implicit Memory in Aging Adults With Mental Retardation With and Without Down Syndrome

2003 ◽  
Vol 108 (4) ◽  
pp. 219 ◽  
Author(s):  
Sharon J. Krinsky-McHale ◽  
Darlynne A. Devenny ◽  
Phyllis Kittler ◽  
Wayne Silverman
Keyword(s):  
Down Syndrome ◽  
Mental Retardation ◽  
Implicit Memory ◽  
Aging Adults
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