Cost-effectiveness analysis of once-weekly semaglutide vs. once-daily liraglutide administered subcutaneously in patients with overweight and obesity: a decision analysis.

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.

A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling.

In this paper, we propose an improved ratio-in-regression type estimator for the finite population mean under stratified random sampling, by using the ancillary varaible as well as rank of the ancillary varaible. Expressions of the bias and mean square error of the estimators are derived up to the first order of approximation. The present work focused on proper use of the ancillary variable, and it was discussed how ancillary variable can improve the precision of the estimates. Two real data sets as well as simulation study are carried out to observe the performances of the estimators. We demonstrate theoretically and numerically that proposed estimator performs well as compared to all existing estimators.

A new asymmetric extended family: Properties and estimation methods with actuarial applications.

In the present work, a class of distributions, called new extended family of heavy-tailed distributions is introduced. The special sub-models of the introduced family provide unimodal, bimodal, symmetric, and asymmetric density shapes. A special sub-model of the new family, called the new extended heavy-tailed Weibull (NEHTW) distribution, is studied in more detail. The NEHTW parameters have been estimated via eight classical estimation procedures. The performance of these methods have been explored using detailed simulation results which have been ordered, using partial and overall ranks, to determine the best estimation method. Two important risk measures are derived for the NEHTW distribution. To prove the usefulness of the two actuarial measures in financial sciences, a simulation study is conducted. Finally, the flexibility and importance of the NEHTW model are illustrated empirically using two real-life insurance data sets. Based on our study, we observe that the NEHTW distribution may be a good candidate for modeling financial and actuarial sciences data.

A discrete analogue of odd Weibull-G family of distributions: properties, classical and Bayesian estimation with applications to count data.

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.

A new statistical approach to model the counts of novel coronavirus cases.

This study proposes new statistical tools to analyze the counts of the daily coronavirus cases and deaths. Since the daily new deaths exhibit highly over-dispersion, we introduce a new two-parameter discrete distribution, called discrete generalized Lindley, which enables us to model all kinds of dispersion such as under-, equi-, and over-dispersion. Additionally, we introduce a new count regression model based on the proposed distribution to investigate the effects of the important risk factors on the counts of deaths for OECD countries. Three data sets are analyzed with proposed models and competitive models. Empirical findings show that air pollution, the proportion of obesity, and smokers in a population do not affect the counts of deaths for OECD countries. The interesting empirical result is that the countries with having higher alcohol consumption have lower counts of deaths.

A one-parameter discrete distribution for over-dispersed data: statistical and reliability properties with applications.

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.

A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates.

This paper proposes a new generalization of the Gull Alpha Power Family of distribution, namely the exponentiated generalized gull alpha power family of distribution abbreviated as (EGGAPF) with two additional parameters. This proposed family of distributions has some well known sub-models. Some of the basic properties of the distribution like the hazard function, survival function, order statistics, quantile function, moment generating function are investigated. In order to estimate the parameters of the model the method of maximum likelihood estimation is used. To assess the performance of the MLE estimates a simulation study was performed. It is observed that with increase in sample size, the average bias, and the RMSE decrease. A distribution from this family is fitted to two real data sets and compared to its sub-models. It can be concluded that the proposed distribution outperforms its sub-models.

A new one-parameter lifetime distribution and its regression model with applications.

Lifetime distributions are an important statistical tools to model the different characteristics of lifetime data sets. The statistical literature contains very sophisticated distributions to analyze these kind of data sets. However, these distributions have many parameters which cause a problem in estimation step. To open a new opportunity in modeling these kind of data sets, we propose a new extension of half-logistic distribution by using the odd Lindley-G family of distributions. The proposed distribution has only one parameter and simple mathematical forms. The statistical properties of the proposed distributions, including complete and incomplete moments, quantile function and Rényi entropy, are studied in detail. The unknown model parameter is estimated by using the different estimation methods, namely, maximum likelihood, least square, weighted least square and Cramer-von Mises. The extensive simulation study is given to compare the finite sample performance of parameter estimation methods based on the complete and progressive Type-II censored samples. Additionally, a new log-location-scale regression model is introduced based on a new distribution. The residual analysis of a new regression model is given comprehensively. To convince the readers in favour of the proposed distribution, three real data sets are analyzed and compared with competitive models. Empirical findings show that the proposed one-parameter lifetime distribution produces better results than the other extensions of half-logistic distribution.

A New Family of Continuous Probability Distributions.

In this paper, a new parametric compound G family of continuous probability distributions called the Poisson generalized exponential G (PGEG) family is derived and studied. Relevant mathematical properties are derived. Some new bivariate G families using the theorems of "Farlie-Gumbel-Morgenstern copula", "the modified Farlie-Gumbel-Morgenstern copula", "the Clayton copula", and "the Renyi's entropy copula" are presented. Many special members are derived, and a special attention is devoted to the exponential and the one parameter Pareto type II model. The maximum likelihood method is used to estimate the model parameters. A graphical simulation is performed to assess the finite sample behavior of the estimators of the maximum likelihood method. Two real-life data applications are proposed to illustrate the importance of the new family.

Statistical inferences for type-II hybrid censoring data from the alpha power exponential distribution.

This paper describes a method for computing estimates for the location parameter µ > 0 and scale parameter λ > 0 with fixed shape parameter α of the alpha power exponential distribution (APED) under type-II hybrid censored (T-IIHC) samples. We compute the maximum likelihood estimations (MLEs) of (µ, λ) by applying the Newton-Raphson method (NRM) and expectation maximization algorithm (EMA). In addition, the estimate hazard functions and reliability are evaluated by applying the invariance property of MLEs. We calculate the Fisher information matrix (FIM) by applying the missing information rule, which is important in finding the asymptotic confidence interval. Finally, the different proposed estimation methods are compared in simulation studies. A simulation example and real data example are analyzed to illustrate our estimation methods.

A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models.

A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model for the bounded response variable. The proposed regression model is compared with the beta and unit-Lindley regression models based on two real data sets.

Exponentiated odd Chen-G family of distributions: statistical properties, Bayesian and non-Bayesian estimation with applications.

In this paper, a new flexible generator of distributions is proposed. Some of its fundamental properties including quantile, skewness, kurtosis, hazard rate function, moments, mean deviations, mean time to failure, mean time between failure, availability and reliability function of consecutive linear and circular systems are studied. The hazard rate function can be increasing, decreasing, unimodal-bathtub, unimodal, bathtub, J and inverse J-shaped depending on its parameters values. After introducing the general class, two special models of the new family are discussed in detail. Maximum likelihood and Bayesian methods are used to estimate the model parameters. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and Bayesian estimators. We also illustrate the importance of the new family by means of two distinctive real data sets. It can serve as an alternative model to other lifetime distributions in the existing statistical literature for modeling positive and negative real data in many areas.

Kumaraswamy inverse Gompertz distribution: Properties and engineering applications to complete, type-II right censored and upper record data.

This article proposes and studies a new three-parameter generalized model of the inverse Gompertz distribution, in the so-called Kumaraswamy inverse Gompertz distribution. The main advantage of the new model is that it has "an upside down bathtub-shaped curve hazard rate function" depending upon the shape parameters. Several of its statistical and mathematical properties including quantiles, median, mode, moments, probability weighted moment, entropy function, skewness and kurtosis are derived. Moreover, the reliability and hazard rate functions, mean time to failure, mean residual and inactive lifetimes are also concluded. The maximum likelihood approach is done here to estimate the new model parameters. A simulation study is conducted to examine the performance of the estimators of this model. Finally, the usefulness of the proposed distribution is illustrated with different engineering applications to complete, type-II right censored, and upper record data and it is found that this model is more flexible when it is compared to well-known models in the statistical literature.

A new two-parameter exponentiated discrete Lindley distribution: properties, estimation and applications.

This paper introduces a new two-parameter exponentiated discrete Lindley distribution. A wide range of its structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, moments, skewness, kurtosis, stress-strength reliability, mean residual lifetime, mean past lifetime, order statistics and L-moment statistics. The hazard rate function can be increasing, decreasing, decreasing-increasing-decreasing, increasing-decreasing-increasing, unimodal, bathtub, and J-shaped depending on its parameters values. Two methods are used herein to estimate the model parameters, namely, the maximum likelihood, and the proportion. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and proportion estimators. The flexibility of the proposed model is explained by using four distinctive data sets. It can serve as an alternative model to other lifetime distributions in the existing statistical literature for modeling positive real data in many areas.

Population Changes of Tylenchulus semipenetrans Under Localized Versus Uniform Drought in the Citrus Root Zone.

Population development of Tylenchulus semipenetrans in dry soil was investigated in a greenhouse study. Citrus seedlings were grown in sandy soil in vertical tubes with upper and lower sections. Nematode population densities in the upper tubes were measured at 16, 23, and 37 days, post-treatment. Three treatments consisted of i) irrigating both tubes when soil water potential reached -1 5 kPa (non-drought), ii) irrigating only the bottom tube (local drought), and iii) no irrigation (uniform drought). Soil water potential in the upper tubes did not differ under local and uniform drought during the first 16 days post-treatment, when it approached - 125 kPa. Thereafter, the water potential of soil under uniform drought continued to decrease, while that under local drought stabilized at approximately -150 kPa. Treatments had no consistent effects on female T. semipenetrans counts from soil or roots. However, after 37 days, numbers of eggs, juvenile, and male nematodes per gram of root under local drought were more than 2.4-fold greater than those under non-drought or uniform drought. Numbers of juvenile and male nematodes in soil were 6.5 times higher under local drought than under non-drought after 37 days. Nematodes did not survive in soil under uniform drought. Most of the eggs recovered on each date, from roots under local and non-drought, hatched within 35 days. Sixteen days of uniform drought reduced cumulative egg hatch to 51%, and almost no eggs hatched after 23 and 37 days of uniform drought. Thus, the response of T. semipenetrans to dry soil is fundamentally different, depending on whether all or part of the rhizosphere experiences drought. These data and field observations suggest that hydraulic lift via the root xylem may prolong the activity of some nematodes and possibly other rhizosphere-inhabiting organisms in dry soil.

Sampling Citrus Fibrous Roots and Tylenchulus semipenetrans.

Sampling precision was investigated for Tylenchulus semipenetrans juveniles and males in soil and females from roots and for citrus fibrous root mass density. For the case of two composite samples of 15 cores each, counts of juvenile and male nematodes were estimated to be within 40% of mu, at P < 0.06 (alpha) in orchards where x > 1,500 nematodes/100 cm(3) soil. A similar level of alpha was estimated for measurements of fibrous root mass density, but at a precision level of 25% of mu. Densities of female nematodes were estimated with less precision than juveniles and males. Precision estimates from a general sample plan derived from Taylor's Power Law were in good agreement with estimates from individual orchards. Two aspects involved in deriving sampling plans for management advisory purposes were investigated. A minimum of five to six preliminary samples were required to appreciably reduce bias toward underestimation of sigma. The use of a Student's t value rather than a standard normal deviate in formulae to estimate sample size increased the estimates by an average of three units. Cases in which the use of z rather than Student's t is appropriate for these formulae are discussed.