Quantile regression shrinkage and selection via the Lqsso

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

Double-blind randomized parallel-group clinical trial of efficacy of the combination fluoxetine plus modafinil versus fluoxetine plus placebo in the treatment of major depression

BACKGROUND

There is growing interest in the identification of novel therapeutic agents for the treatment of affective disorders, with modafinil being one promising substance. The purpose of the present investigation was to compare the efficacy of a combination of fluoxetine plus modafinil with that of fluoxetine plus placebo in the treatment of major depression in a 6-week double blind and placebo-controlled trial.

METHODS

Forty-six adult outpatients who met the DSM-IV-TR criteria for major depression participated in the trial. Patients had a baseline Hamilton Rating Scale for depression score of at least 18. Patients were allocated in a random fashion, 23 to fluoxetine 40 mg/day plus modafinil 400 mg/day (200 mg bid) (morning and evening) and 23 to fluoxetine 40 mg/day plus placebo. Patients were assessed at baseline and after 1, 2, 4, and 6 weeks after start of medication.

RESULTS

Forty-four patients completed the trial. Fluoxetine+modafinil and fluoxetine+placebo significantly decreased the Hamilton Rating Scale score for Depression over the trial period. However, the combination of fluoxetine and modafinil was significantly superior over fluoxetine alone in the treatment of symptoms of major depression. The difference between the two treatments was significant as indicated by the effect of group, the between-subjects factor (df = 1, F = 4.42, P = 0.046). There were no significant differences in the two groups in terms of observed side-effects.

CONCLUSION

These findings suggest modafinil as a well-tolerated and potentially effective agent in combination with fluoxetine in the management of patients with major depression.

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.