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OBJECTIVE: Obesity presents an enduring and multifaceted dilemma that impacts individuals, society, economies, and healthcare systems alike. Glucagon-like peptide-1 (GLP-1) receptor agonists, including liraglutide and semaglutide, have received FDA approval for obesity treatment. This study aims to present a cost-effectiveness analysis to compare the cost and clinical outcomes of semaglutide vs. liraglutide on weight loss in people with overweight and obesity. MATERIALS AND METHODS: A cost-effectiveness analysis was conducted to compare the cost and the clinical outcomes of adding weekly 2.4 mg SC semaglutide vs. daily 3.0 mg SC liraglutide or placebo to physical activity and diet control in overweight and obese patients. A clinical outcome of achieving =15% weight loss was chosen. A simple decision analysis model from a third-payer perspective was applied. Drug costs were based on the retail price of the USA market. One-way sensitivity analyses were performed. RESULTS: Results showed that 2.4 mg weekly semaglutide, when added to physical activity and diet control, was the most cost-effective choice in terms of =15% weight loss (ICER: $ 7,056/patient/68 weeks). The model was robust against the 50% increase in the unit cost of semaglutide and the 50% decrease in the unit cost of liraglutide, as well as the changes in probabilities by the corresponding 95% confidence intervals across the model. CONCLUSIONS: This cost-effectiveness analysis suggests that employing once-weekly 2.4 mg semaglutide emerges as a remarkably cost-effective option when contrasted with once-daily 3.0 mg liraglutide in patients with overweight and obesity when added to physical activity and diet control. [ABSTRACT FROM AUTHOR]

In this paper, we consider the stochastic Burgers' equation, which is forced by multiplicative noise in the Stratonovich sense. To get a new trigonometric and hyperbolic stochastic solutions, we apply $ \exp (-\varphi (\mu)) $ exp ⁡ (− φ (μ)) -expansion method. In addition, we demonstrate the effect of multiplicative noise on the exact solutions of the Burgers' equation by introducing some graphic representations. [ABSTRACT FROM AUTHOR]

In this paper, a new two-parameter discrete logistic exponential distribution is proposed based on the survival discretization approach. Some statistical properties are derived, and it is found that the proposed model can be used to discuss several kinds of failure rates including unimodal, bathtub, and increasing-shaped. Moreover, it can be utilized effectively to model under- and over-dispersed data. The distribution parameters are estimated using the maximum likelihood technique. The behavior of maximum likelihood estimators is assessed utilizing a comprehensive simulation study. In the end, two real data are analyzed to show the usefulness of the new discrete distribution. [ABSTRACT FROM AUTHOR]

In the Itô sense, we look at the stochastic Maccari system (SMS) with multiplicative noise. To generate new analytical stochastic solutions, we use three separate techniques including sine‐cosine, Riccati‐Bernoulli sub‐ODE, and semi‐inverse. Such solutions are critical for comprehending some fascinating and complex physical phenomena. In addition, some results from earlier studies are generalized. Furthermore, we investigate the impact of stochastic term on the exact solutions of the SMS. [ABSTRACT FROM AUTHOR]

In the statistical literature, several discrete distributions have been developed so far. However, in this progressive technological era, the data generated from different fields is getting complicated day by day, making it difficult to analyze this real data through the various discrete distributions available in the existing literature. In this context, we have proposed a new flexible family of discrete models named discrete odd Weibull-G (DOW-G) family. Its several impressive distributional characteristics are derived. A key feature of the proposed family is its failure rate function that can take a variety of shapes for distinct values of the unknown parameters, like decreasing, increasing, constant, J-, and bathtub-shaped. Furthermore, the presented family not only adequately captures the skewed and symmetric data sets, but it can also provide a better fit to equi-, over-, under-dispersed data. After producing the general class, two particular distributions of the DOW-G family are extensively studied. The parameters estimation of the proposed family, are explored by the method of maximum likelihood and Bayesian approach. A compact Monte Carlo simulation study is performed to assess the behavior of the estimation methods. Finally, we have explained the usefulness of the proposed family by using two different real data sets. [ABSTRACT FROM AUTHOR]

In the literature of distribution theory, a vast proportion is acquired by discrete distributions and their applications in real-world phenomena. However, in a rapidly changing technological era, the data generated is becoming increasingly complex day by day, making it difficult for us to capture various aspects of this real data through existing discrete models. In view of this, we propose a new flexible discrete distribution with one parameter. Some statistical and reliability are derived. These properties can be expressed as closed-forms. One of the important virtues of this newly evolved model is that it can model not only over-dispersed, positively skewed and leptokurtic data sets, but it can also be utilized for modeling increasing, decreasing and unimodal failure rate. Various estimation approaches are utilized to estimate the model parameter. A simulation study is carried out to examine the performance of the estimators for different sample size. The flexibility of the new model for analyzing different types of data is explained by utilizing four real data sets in different fields. Finally, the proposed model can serve as an alternative model to other distributions in the existing literature for modeling positive real data in several areas. [ABSTRACT FROM AUTHOR]