Aiding the well celebrated Kuk's randomized response technique through auxiliary and prior information.

Asking direct questions in face to face surveys about sensitive traits is an intricate issue. One of the solutions to this issue is the randomized response technique (RRT). Being the most widely used indirect questioning technique to obtain truthful data on sensitive traits in survey sampling RRT has been applied in a variety of fields including behavioral science, socio-economic, psychological, epidemiology, biomedical, criminology, data masking, public health engineering, conservation studies, ecological studies and many others. This paper aims at exploring the methods to subsidize the randomized response technique through additional information relevant to the parameter of interest. Specifically, we plan to contribute by proposing more efficient hybrid estimators compared to existing estimator based on (Kuk, 1990) [31] family of randomized response models. The proposed estimators are based on the methodology of incorporating the pertinent information, available on the basis of either historical records or expert opinion. Specifically, in case of availability of auxiliary information, the regression-cum-ratio estimator is found to be the best to further enhance the estimation through (Kuk, 1990) [31] model while the (Thompson, 1968) [49] shrinkage estimation is observed to be yielding more precise and accurate estimator of sensitive proportion. The findings in this study signify the importance of the proposed methodology. Additionally, to support the mathematical findings, a detailed numerical investigation to evaluate the comparative performances is also conducted. Based on performance analysis, overwhelming evidences are witnessed in the favor of proposed strategies.

Use of improved memory type control charts for monitoring cancer patients recovery time censored data.

Control charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.

A new unit distribution: properties, estimation, and regression analysis.

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson-Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Estimation of regional and at-site quantiles of extreme winds under flood index procedure.

Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessary not only for a comprehensive understanding of wind hazards but also for infrastructure design and safety, wind energy potential, disaster risk reduction, insurance and risk assessment in a particular region of study. This study investigated regional frequency analysis of DAMWS of Baluchistan and Sindh provinces of Pakistan. L-moments regionalization techniques along with flood index procedure were applied to DAMWS records of 21 stations from 1990 to 2019 across the study area. We intended to find the regional frequency distribution for maximum winds and predict the returns for extreme winds events in the future. Only one station namely Lasbella was found to be discordant. With the help of cluster analysis, the remaining 20 stations were further divided into two homogeneous. Heterogeneity measures validate that both regions are homogenous with allotted stations. Regional quantiles for both regions are estimated through best-fit probability distribution among Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type 3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV). Robustness of GLO distribution compared to GEV distribution is assessed through Monte Carlo simulations of relative bias and relative root mean square error. Findings clearly show that GLO distribution is the best for regional modeling. Furthermore, with the help of index flood procedure we determined at-site quantiles of all stations for various return periods. These estimated quantiles are of valuable information for various sectors, including infrastructure, energy, disaster management, and climate resilience, leading to improved planning, development, and risk reduction in the face of wind-related hazards in Sindh and Balochistan provinces of Pakistan.

Trends in Monkeypox transmission: Investigation into 30 most affected countries.

This research is based on the analysis of Monkeypox transmission, from may 7, 2022 to October 11, 2022, in 30 most affected countries around the globe. The most affected countries are selected through the cut point of at least 100 reported confirmed cases of infected persons over the aforementioned time span. We novely argue the exhibition of distributional similarities between the viral flow and well known power law in context of this neglected zoonotic disease. Moreover, model-based evidence suggesting the capability of pathogen to spread far and wide around its nucleus, are collected and presented. It is estimated that 70 % of the reported confirmed cases belonged to 20 % of the top most affected countries. Also, 70 % of the reported transmission was inflicted in 34 % of the days of reporting at least one case, on average.

Single nucleotide polymorphisms in cytokine genes and their association with primary Sjögren's syndrome in Saudi patients: A cross-sectional study.

OBJECTIVES: To determine the allelic frequencies and effects of genotypic variations in cytokine gene polymorphisms in a Saudi Arabian population. METHODS: This cross-sectional study involved 41 patients with Primary Sjögren's syndrome (pSS) and 71 healthy controls between October 2018 and May 2019. Single nucleotide polymorphisms genotyping was performed using the SEQUENOM MassARRAY® System, targeting nine polymorphisms in different cytokine genes. Chi-square tests were used to compare the patients and controls. RESULTS: The interleukin-1 beta (IL-1ß) rs1143627 CT (control, 52.7%; patients, 21.2%) and TT + CT (p= 0.003; p=0.033) genotypes were less frequent in patients with pSS than in healthy controls. The C allele in rs10488631 in the interferon regulatory factor 5 (IRF5) gene and the A allele in rs12583006 in the B-cell activating factor (BAFF) gene were associated with an increased risk of pSS development in the patient group. CONCLUSION: The CT genotype at -31 (rs1143627) in the IL-1ß gene was not associated with a high risk of pSS development in the Saudi population, in contrast to what has been verified in other ethnicities. However, the C allele in rs10488631 in IRF-5 and the A allele in rs12583006 in BAFF were associated.

Adaptive EWMA control chart for monitoring the coefficient of variation under ranked set sampling schemes.

In this study, we introduce an Adaptive Exponentially Weighted Moving based Coefficient of Variation (AEWMCV) control chart, designed to address situations where the process mean fluctuates over time and the standard deviation of the process changes linearly with the process mean. To enhance the efficiency and effectiveness of the control chart, we integrate the ranked set sampling method and its modified schemes, such as Simple Random Sampling, Quartile RSS, Median RSS, and Extreme RSS. The performance of the proposed AEWMCV control chart and the studied CV control charts are evaluated using the Average Run Length and Standard Deviation of Run Length metrics. Our findings reveal that the proposed control chart outperforms the existing CV control charts, especially in detecting slight to moderate changes in the process CV. To illustrate the practical applicability of the suggested control chart, we present an example demonstrating its use on a real dataset. The results highlight the superior performance of the AEWMCV control chart in accurately detecting and responding to changes in the process CV. In conclusion, our study introduces an innovative AEWMCV control chart that combines ranked set sampling and its modified schemes to enhance performance in scenarios with fluctuating process means and changing standard deviations. The proposed control chart proves to be more effective in detecting subtle variations in the process CV compared to traditional CV control charts. This research provides a valuable contribution to the field of control chart methodology, especially when dealing with challenging or costly data collection scenarios.

Adaptive multivariate dispersion control chart with application to bimetal thermostat data.

Adaptive EWMA (AEWMA) control charts have gained remarkable recognition by monitoring productions over a wide range of shifts. The adaptation of computational statistic as per system shift is the main aspect behind the proficiency of these charts. In this paper, a function-based AEWMA multivariate control chart is suggested to monitor the stability of the variance-covariance matrix for normally distributed process control. Our approach involves utilizing an unbiased estimator applying the EWMA statistic to estimate the process shift in real-time and adapt the smoothing or weighting constant using a suggested continuous function. Preferably, the Monte Carlo simulation method is utilized to determine the characteristics of the suggested AEWMA chart in terms of proficient detection of process shifts. The underlying computed results are compared with existing EWMA and existing AEWMA charts and proved to outperform in providing quick detection for different sizes of shifts. To illustrate its real-life application, the authors employed the concept in the bimetal thermostat industry dataset. The proposed research contributes to statistical process control and provides a practical tool for the solution while monitoring covariance matrix changes.

Association of Inflammatory Cytokine Levels with Extra Glandular Manifestations, Fatigue, and Disease Activity in Primary Sjögren's Syndrome in Saudi Patients: A Cross-Sectional Study.

BACKGROUND: Primary Sjögren's syndrome (pSS) is an autoimmune disease that can cause fatigue and extraglandular manifestations (EGMs). pSS is associated with cytokine network dysregulation, which may be related to the immune-mediated destruction of exocrine glands. OBJECTIVE: We determined cytokine levels and their relationship to EGMs, the European League Against Rheumatism (EULAR) Sjögren's syndrome disease activity index (ESSDAI), and fatigue in Saudi patients with pSS. METHODS: This study was a cross-sectional, single-center study. We included forty-one patients and 71 controls. Serum samples were collected from random healthy people and pSS patients who were followed in the rheumatology and pulmonary clinics of King Saud University Medical City in Riyadh, Saudi Arabia. Levels of the frequently studied cytokines were measured using Luminex xMAP technology. Each ESSDAI score and EGM were recorded, and the Arabic version of the fatigue severity scale (FSS) was applied to assess fatigue. The main outcome measures were cytokine levels in pSS Saudi patients using/not using immune-suppressive medications (ISMs). RESULTS: Thirty-six (87.8%) patients had one or more EGMs, and the mean ESSDAI score was 9.95 ± 7.73. There was a significant decrease in TNFα and IL-21 levels in the pSS group compared to those in the control group (p = 0.034 and p < 0.001, respectively), whereas IL-12 levels were significantly elevated in the pSS group (p = 0.002). Cytokine levels in patients who used ISMs were the same as those in patients who did not use medications. Decreased IL-1ß (p = 0.014), IL-2 (p = 0.035), IL-6 (p = 0.014), and IL-35 (p = 0.010) levels were observed in patients who had EGMs. Patients who had low disease activity exhibited low IL-10 (p = 0.018) and high IFN-α (p = 0.049), IFN-ß (p = 0.049), IL-1ß (p = 0.006), and IL-35 (p = 0.032) levels compared to patients with high disease activity. A negative association between a positive fatigue score and IL-1ß (p = 0.010), IL-2 (p = 0.037), IFN-α (p = 0.025), TNFα (p = 0.030), IL-17 (p = 0.029), IL-12 (p = 0.046), and IL-21 (p = 0.005) levels was found. CONCLUSIONS: Cytokine profiles correlate with EGMs, ESSDAI, and fatigue. Patients with controlled disease activity have a normal cytokine profile that is similar to that of controls.

A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute.

The article introduces a novel class of estimators designed for estimating finite population proportions. These estimators utilize dual auxiliary attributes and are applicable under simple random sampling. The proposed class of estimators includes various members with distinct characteristics. The article provides numerical terminologies for the bias and MSE of the estimators, acquire up to first order of approximation. Four actual data sets are used. Additionally, a simulation study is accompanied to perceive the presentations of estimators. The MSE criterion is used to assess how well the proposed estimator performed as likened to the preliminary estimators. The simulation analysis revealed that, in contrast to other examined estimators, the suggested class of estimators provided better results. The empirical investigation offers evidence to substantiate the findings of the argument. Theoretical research also displays that the suggested class of estimators outperforms its competitors.

Disease phenotype and diagnostic delay in Saudi patients with primary Sjögren's syndrome: An exploratory cross-sectional study.

OBJECTIVES: To describe primary Sjögren's syndrome (pSS) cohort in Saudi Arabiain view in of clinical/serological/histopathological phentotype, and, diagnostic delay. METHODS: A cross-sectional study conducted between October 2018 and May 2019. Diagnostic delay was calculated from symptoms onset to clinical diagnosis. The European League Against Rheumatism (EULAR) Sjögren's Syndrome Disease Activity Index (ESSDAI) and EULAR Sjogren's Syndrome Patient Reported Index (ESSPRI) were calculated. RESULTS: Forty-one patients were included in the study. There were predominantly females (78%) with a mean (±SD) age of 58.76±12.7 and disease duration of 4.6±2.28 years. The mean diagnostic delay was 2.2±2.4 (range 1-11) years. Minor salivary gland biopsy was performed on 38 (92.7%) patients with a mean focus score of 2.3± 1.2 points. Interstitial lung disease and arthritis were the most common extra-glandular manifestations (EGM) affecting 27 (65.9%) patients for both. The mean ESSDAI was 9.95±7.73 and ESSPRI was 5.17±2.4. CONCLUSION: Saudi primary Sjogren's syndrome patients have a high prevalence of EGM predominantly arthritis and ILD. The diagnostic delay is variable in our cohort.

Fatigue in Saudi Patients with Primary Sjögren's Syndrome and Its Correlation with Disease Characteristics and Outcome Measures: A Cross-Sectional Study.

BACKGROUND: Fatigue is a prevalent symptom affecting primary Sjögren's syndrome (pSS) patients. The purpose of this study is to determine the prevalence of fatigue in Saudi pSS patients and its correlation with disease features and outcome measures using a validated tool. METHODS: This is a cross-sectional study evaluating fatigue in pSS using the Arabic version of the fatigue severity scale (FSS). The EULAR Sjögren's syndrome disease activity index (ESSDAI) and EULAR Sjögren's syndrome patient reported index (ESSPRI) were calculated. RESULTS: Forty-one patients met the sample criteria and were involved in the final report. There were predominantly females (78%) with a mean (±SD) age and disease duration of 58.76±12.7 and 4.6±2.28 years, respectively. Based on the FSS, 18 (43.9%) patients had a positive test with a mean score of 5.43±0.76. The mean ESSDAI was 9.95±7.73, while the mean EESPRI was 5.17±2.4 with individual component scores were dryness (5.23±2.62), fatigue (5.4±2.88), and pain (4.88±3.31). The FSS had a significant correlation with PGA (r=0.559; p<0.001), PhGA (r=0.671; p<0.001), ESSDAI (r=0.402; p=0.01), ESSPRI fatigue component (r=0.0.621; p<0.001), ESSPRI pain component (r=0.558; p<0.001), and missed significance for the ESSPRI dryness component (r=0.289; p=0.071). There was no correlation between the total ESSPRI score and presence of fatigue (r=-0.261; p=0.104) nor the FSS score (r=-0.136; p=0.409). CONCLUSION: Fatigue is prevalent in Saudi pSS patients. FSS correlated with ESSDAI and ESSPRI components but not its total score signaling other unmeasured factors contributing to fatigue development.