Bayesian separate and joint modeling for controlled clinical trial data using BUGS. In: Applied Bayesian Statistical Analysis

Many clinical trials and other medical studies generate both longitudinal (repeated measurements) and survival (time to event) data. The existing methods are inappropriate when the longitudinal variable is correlated. Earlier articles proposed a joint model for longitudinal and survival data, obtaining maximum likelihood estimates via the EM algorithm based on Bayesian approach implementing via Markov Chain Monte Carlo (MCMC) methods. The longitudinal and survival responses are assumed independent given a linking latent bivariate Gaussian process and available covariates. We use the approach to jointly model the longitudinal and survival data from a clinical trial comparing treatments and also its interactions. The joint Bayesian approach appears to offer significantly improved and enhanced estimation of survival times and other parameters of interest like gender, age and weight. In spite of the complexity the model, we find it to be relatively straight forward to implement and understand using the WinBUGS software

Vaginal deliveries in a tertiary centre: a current profile

Background: A normal delivery is what every woman wishes to have. The objective of this study was to find out the maternal and neonatal outcome and background characteristics of women delivering vaginally in a tertiary care center in Chennai, South India.Methods: For this one-year study, with power above 80%, Parturition records were selected by computerized random numbers, for a calculated sample size. Salient demographic features such as age, residential background and religion were noted. Details of obstetric history, past and current, delivery and baby details and admission to NICU were analyzed. Acceptance of postpartum contraception was noted.Results: A total of 338 women delivered vaginally. Majority of 63%, were from urban background. Late referrals were 19.2% of women,38.5% women had antenatal complications. Primigravida were 49.7%. Nearly 91.4% of women delivered naturally. Previous pregnancy loss was noted in 14.8%. Term deliveries were in 72% of women, and 2.7% of women delivered twins. Average birth weight among primi was 2.5kg and in multi it was 2.8 kg. There were no maternal deaths. Perinatal deaths of 2.96%, of which 90% were preterm births, and all among babies with birth weight below 1.5 kg.Conclusions: The larger majority of 91.4% of women had natural vaginal delivery. Primigravida were 49.7%, and 63% were from urban background. Antenatal complications, obstetric, medical or other complications were noted in 38.5 % of women. Most often observed complications were Gestational hypertension, Gestational diabetes, and Hypothyroidism. NICU care was required for 18% of babies. Preterm births were16.6%. Perinatal deaths were seen in 2.96% of babies. There were no maternal deaths

Neural network based tuberculosis disease classification

Symptoms based Tuberculosis disease diagnosis is one of the challenging tasks in the medical field. So many techniques are availabe for classification of data such as Artificial Neural Network, Support vector machine and Genetic Algorithm. The objective of this paper is to construct a Multiplayer Feed Forward Neural Network model for the diagnosis of Tuberculosis. The trained network serves as a knowledge base of the system. The construction of the system is presented in this paper. This model correctly classifies 92.3%