Nadia Saeed

Background/Objectives: Chronic kidney disease (CKD) is a severe health problem with dire consequences for the quality of life of millions of individuals and their families around the globe. This quantitative study analyzes the factors associated with hemodialysis patients’ quality of life (QoL) in Lahore City, Pakistan. Methods: Primary data from a sample of 384 patients were collected through regular visits to the hospital. We employed proportional odds models (POMs) and structural equation models to identify factors associated with the QoL. Results: The results revealed significant associations between various factors and patients’ quality of life. While gender showed no association with quality of life, younger age, single marital status, higher education, higher family income, and employment status were associated with a better QoL. Clinical variables such as the absence of diabetes and hypertension and specific laboratory parameters were protective against deteriorating QoL. Physical symptoms like muscle soreness, cramps, and shortness of breath significantly impacted QoL. Social and environmental factors adversely affected patient well-being, including family distress and financial issues. Psychological variables such as anxiety, depression, and fear of death also influenced QoL. Conclusions: The findings underscore the importance of holistic, patient-centered care approaches in renal failure management, highlighting the need for tailored interventions to address the diverse needs of dialysis patients and enhance their QoL. Further longitudinal research is recommended to validate these findings and guide the development of targeted interventions for improving patient well-being in hemodialysis settings.

While researchers and practitioners are seamlessly trying to develop methods for minimizing the effect of outliers in control charts, detecting and screening these outliers continue to pose serious challenges. Keeping in view, the researchers rely on robust estimators to modify the detection limits structure so that the chart can be more sensitive against outliers. In this study, we propose a robust control chart based on , , , , and estimators, whilst the process parameter is estimated from Phase‐I. Through intensive Monte‐Carlo simulations, the study presents how the estimation of parameter(s) and presence of outliers affect the efficacy of the chart, and then how the proposed outlier detectors bring the chart back to normalcy by restoring its efficacy and sensitivity. Average properties are used as the performance measures. The properties establish the superiority of the proposed scheme over and Tukey's outlier detectors. The applicability of the study includes the effectiveness of the proposed detectors in industrial data set but is not limited to manufacturing industries.

Robust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process.

In the recent years, another approach named as the bootstrap method is getting popular in Statistical Process Control (SPC) specifically when the underlying distribution of the process is unknown. The bootstrap estimators are getting popularity in statistical process control due to their remarkable properties for non-normal distribution. In this paper the bootstrap control chart is developed for monitoring process variability and robustness is discussed through simulation studies. It appears that the proposed control chart for monitoring process variability based on the bootstrap method is performing better to detect out-of-control signal in a case when data follow skewed distributions. Therefore, the proposed chart is more recommendable for industrial practitioners.

A new scheme ‘Rhombus Ranked Set Sampling’ (RRSS) is developed in this research together with its properties for estimating the population means. Mathematical validation along with the simulation evaluation is presented. The proposed method is an addition to the family of different sampling methods and generalization of ‘Folded Ranked Set Sampling’ (FRSS). For the simulation process, nine probability distributions are considered for the efficiency comparison of proposed scheme from which four are symmetric and rest are asymmetric among which Weibull and beta distributions which are used twice, unlike parametric values. (Al-Naseer, 2007 and Bani-Mustafa, 2011). Through simulation processes, it is observed that RRSS is competent and more reliable relative to simple random sampling (SRS), ranked set sampling (RSS) and folded ranked set sampling (FRSS). It is noted that for all the underlying distributions, an increase in the efficiency of Rhombus Ranked Set Sampling (RRSS) is achieved via increasing the size of the sample ‘p’. Besides the efficiency comparison, consistency of the proposed method is also valued by using Co-efficient of Variation (CV). Secondary data on zinc (Zn) concentration and lead (Pb) contamination in different parts and tissues of freshwater fish was collected to illustrate the evaluation of RRSS against SRS, RSS, FRSS and ERSS (extreme ranked set sampling). The results obtained through real life illustration defend the simulation study and hence indicates that the RRSS estimator is efficient substitute for existing methods (Al-Omari, 2011).