Abstract:Objective: Sleep disturbance and excessive daytime sleepiness have been reported in patients with hepatic cirrhosis. The objective of this study was to evaluate daytime somnolence and sleep complaints in a group of 178 patients with cirrhosis compared to a control group.Method: Sleep features and excessive daytime sleepiness were evaluated by the Basic Nordic Sleep Questionnaire (BNSQ) and the Epworth Sleepiness Scale (ESS). We collected clinical and laboratory data, neurological assessment and EEG recording in cirrhotic patients. Results:Patients with cirrhosis complained of more daytime sleepiness (p<0.005), sleeping badly at least three times a week (p<0.005), difficulties falling asleep (p<0.01) and frequent nocturnal awakening (p<0.005) than controls. We found a poor correlation between sleep disorders and clinical or laboratory parameters. Conclusion: Our results confirm previous literature reports suggesting a high prevalence of sleep disturbance in patients with cirrhosis.Insomnia and daytime sleepiness are the main complaints. Sleep disorders are probably a multifactorial phenomenon.

Efron's biased coin design is a well-known randomization technique that helps to neutralize selection bias in sequential clinical trials for comparing treatments, while keeping the experiment fairly balanced. Extensions of the biased coin design have been proposed by several researchers who have focused mainly on the large sample properties of their designs. We modify Efron's procedure by introducing an adjustable biased coin design, which is more flexible than his. We compare it with other existing coin designs; in terms of balance and lack of predictability, its performance for small samples appears in many cases to be an improvement with respect to the other sequential randomized allocation procedures. Copyright 2004 Royal Statistical Society.

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This paper deals with optimal designs for Gaussian random fields with constant trend and exponential correlation structure, widely known as the Ornstein-Uhlenbeck process. Assuming the maximum likelihood approach, we study the optimal design problem for the estimation of the trend and the correlation parameter using a criterion based on the Fisher information matrix. For the problem of trend estimation, we give a new proof of the optimality of the equispaced design for any sample size (see Statist. Probab. Lett. 2008; 78:1388-1396. We also show that for the estimation of the correlation parameter, an optimal design does not exist. Furthermore, we show that the optimal strategy for conflicts with the one for , since the equispaced design is the worst solution for estimating the correlation. Hence, when the inferential purpose concerns both the unknown parameters we propose the geometric progression design, namely a flexible class of procedures that allow the experimenter to choose a suitable compromise regarding the estimation's precision of the two unknown parameters guaranteeing, at the same time, high efficiency for both.

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