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.

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.