6-Sulfatoxymelatonin as a predictor of clinical outcome in depressive patients

Objectives This study established the value of the 6-sulfatoxymelatonin (aMT6s) urine concentration as a predictor of the therapeutic response to noradrenaline reuptake inhibitors in depressive patients. Methods Twenty-two women aged 18-60 years were selected. Depressive symptoms were assessed by using the Hamilton Depression Scale. Urine samples were collected at 0600-1200 h, 1200-1800 h, 1800-2400 h, and 2400-0600 h intervals, 1 day before and 1 day after starting on the nortriptyline treatment. Urine aMT6s concentration was analyzed by a one-way analysis of variance/Bonferroni test. Spearman`s rank correlation coefficient was used to analyze the correlation between depressive symptoms after 2 weeks of antidepressant treatment and the increase in aMT6s urine concentration. Results Higher and lower size effect groups were compared by independent Student`s t-tests. At baseline, the 2400- to 0600-h interval differed from all other intervals presenting a significantly higher aMT6s urine concentration. A significant difference in aMT6s urine concentrations was found 1 day after treatment in all four intervals. Higher size effect group had lower levels of depressive symptoms 2 weeks after the treatment. A positive correlation between depressive symptoms and the delta of aMT6s in the 2400-0600h interval was observed. Conclusion Our results reinforce the hypothesis that aMT6s excretion is a predictor of clinical outcome in depression, especially in regard to noradrenaline reuptake inhibitors. Copyright (C) 2011 John Wiley & Sons, Ltd.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Estimation of the mean quality-adjusted survival using a multistate model for the sojourn times

In clinical trials, it may be of interest taking into account physical and emotional well-being in addition to survival when comparing treatments. Quality-adjusted survival time has the advantage of incorporating information about both survival time and quality-of-life. In this paper, we discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models for the sojourn times in health states. Semiparametric and parametric (with exponential distribution) approaches are considered. A simulation study is presented to evaluate the performance of the proposed estimator and the jackknife resampling method is used to compute bias and variance of the estimator. (C) 2007 Elsevier B.V. All rights reserved.