Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.     

Majorization and the spectral radius of starlike trees

A starlike tree is a tree with exactly one vertex of degree greater than two. The spectral radius of a graph G, that is denoted by \(\lambda (G)\), is the largest eigenvalue of G. Let k and \(n_1,\ldots ,n_k\) be some positive integers. Let \(T(n_1,\ldots ,n_k)\) be the tree T (T is a path or a starlike tree) such that T has a vertex v so that \(T{\setminus } v\) is the disjoint union of the paths \(P_{n_1-1},\ldots ,P_{n_k-1}\) where every neighbor of v in T has degree one or two. Let \(P=(p_1,\ldots ,p_k)\) and \(Q=(q_1,\ldots ,q_k)\), where \(p_1\ge \cdots \ge p_k\ge 1\) and \(q_1\ge \cdots \ge q_k\ge 1\) are integer. We say P majorizes Q and let \(P\succeq _M Q\), if for every j, \(1\le j\le k\), \(\sum _{i=1}^{j}p_i\ge \sum _{i=1}^{j}q_i\), with equality if \(j=k\). In this paper we show that if P majorizes Q, that is \((p_1,\ldots ,p_k)\succeq _M(q_1,\ldots ,q_k)\), then \(\lambda (T(q_1,\ldots ,q_k))\ge \lambda (T(p_1,\ldots ,p_k))\).     

Measuring Human,Physical and Structural Capital Efficiency Performance of Insurance Companies

Based on the strategy perspective of intellectual capital, this paper proposes an integrated framework that is practical to estimate the human, physical, and structural capitals (intellectual capital) efficiency performance, at firm level. This paper uses a dynamic network data envelopment analysis model to estimate the intellectual capital efficiency at three levels in the insurance industry over the period of 2005–2012. Within the insurance industry, deficiencies occurred in the human and structural capital stages as opposed to the physical capital stage. A further investigation indicates that total investment is the major concern for the deficiencies. Moreover, the cluster analysis highlights the strengths and weaknesses of the insurers based on their inherently similar efficiencies. Malaysian insurance industry need to translate and promote the existing knowledge-based economy agenda lauded by the government to improve the intellectual capital efficiency, particularly at the human capital level.     

A mixed-effects least square support vector regression model for three-level count data

Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.     

Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500?g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.     

Testing the difference between spectral densities of two independent periodically correlated (cyclostationary) time series models

In this work, the asymptotic distribution for the discrete Fourier transform of periodically correlated (PC) processes is applied to test the equality of two PC time series. Then the performance of the proposed method is investigated through the Monte Carlo simulations.     

Comparing the transpirational and shading effects of two contrasting urban tree species

Urban Ecosystems - Urban trees are getting increasing attention as a tool to mitigate urban heat island effects. A more functional and quantitative view of transpirational and shading effect,...     

Relationships between psychological capital,job satisfaction,and organizational commitment in the Saudi oil and petrochemical industries

During the past decade, Saudi Arabia experienced a significant social, economic, and organizational change. The rapid economic growth created a need for seasoned management professionals and necessitated the development of human capital. Psychological capital, a newly developed construct by academics and practitioners, is defined as the extent to which an individual operates in a positive psychological state, and this state is characterized by high self-efficacy, optimism, hope, and resiliency. By measuring the positive psychological constructs, an organization can learn about employees’ positive psychological states and how training and support can promote positive psychological states. Improving the positive psychological capital can lead to better organizational commitment, favorable organizational citizenship behaviors, lower employee absenteeism, and higher job satisfaction. This quantitative study examined the relationship among psychological capital, job satisfaction, and organizational commitment through a sample of managers in the Saudi Arabian oil and petrochemical industries.