Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

Maternal factors, fetal parameters and the risk of shoulder dystocia

Introduction. Shoulder dystocia is a serious complication of vaginal birth, with an incidence ranging from 0.15% to 2.1% of all births. There are approximately 4 million births per year in the United States and shoulder dystocia will be experienced by approximately 20,000 women each year. Although studies have been reported on shoulder dystocia, few studies have addressed both maternal and fetal risk factors. The purpose of this study was to identify maternal and fetal risk factors for shoulder dystocia while proposing factors that could be used to predict impending shoulder dystocia. ^ Material and methods. Articles were reviewed from Medline Pubmed using the search phrase "Risk factors of shoulder dystocia" and Medline Ovid using the search words "Dystocia", "Shoulder" and "Risk factors". Rigorous selection criteria were used to identify articles to be included in the study. Data collected from identified articles were transferred to STATA 10 software for trend analysis of the incidence of shoulder dystocia and the year of publication and a pair wise correlation was also determined between these two variables. ^ Results. Among a total of 343 studies identified, only 20 met our inclusion criteria and were retained for this review. The incidence of shoulder dystocia ranged from 0.07% to 2% and there was no particular trend or correlation between the incidence of shoulder dystocia and year of publication between 1985 and 2007. Pre-gestational and gestational diabetes, postdatism, obesity, birth weight > 4000g and fundal height at last visit > 40cm were identified as major risk factors in our series of studies. ^ Conclusion. Future strategies to predict shoulder dystocia should focus on pre-gestational and gestational diabetes mellitus, postdatism, obesity, birth weight > 4000g and fundal height at last visit > 40cm. ^

LONGITUDINAL GROWTH PARAMETERS AND THEIR ECOLOGICAL DETERMINANTS (SEASONALITY)

The growth patterns of weight from birth through the first twelve months of life among rural Taiwanese infants were investigated with the following objectives: (i) compare each of the parameters of the Count model estimated for infants who were nutritionally at risk with those for a reference population from the United States; and (ii) within the Taiwanese infants, account for the variance in the growth patterns in the first and second six months of life on the basis of selected ecological factors.^ The significance between group differences were observed in the patterns of the weight growth in both linear growth and in the timing and the direction of velocity changes. A significant decline in growth velocity was observed among Taiwanese infants at about the fourth month of life. The decline is in keeping with a recent proposal made by J. C. Waterlow regarding the timing of change in growth velocity among nutritionally at risk populations in developing countries. The growth course of a nutritionally at risk infant during the first three months is apparently protected by the nurturance of the mother and innate biological properties of the infant.^ A highly significant portion of the growth variance in the second six months of life was accounted for by exogenous factors and biological factors related to the infant. Conversely, none of the growth variance in the first six months of life was accounted for by predictor variables. The most potent determinant of growth in the second six months of life was seasonality which represents a multiple environmental event.^ The model parameters estimated from the Count model represent different aspect of physical growth; yet the correlation coefficients between parameters b and c are high (r > .80). Clearly, the biological interpretation of the model parameters requires analysis of the whole function in the specific context of a given age period. ^