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.

Nexus between fossil fuel consumption and infant mortality rate: a non-linear analysis.

For nations already facing pollution issues, fossil fuel energy consumption is deteriorating living standards. Though prevailing infrastructure makes fossil fuel energy use feasible for production, it still pollutes our environment. This study in the domain of environment and development assesses the impact of fossil fuel energy consumption on the infant mortality rate in 15 Asian economies during 1996 and 2019. The living standards are assessed using infant mortality rate, while non-linear fossil fuel energy consumption is used with gross capital formation, employment, and preprimary education as independent variables. The feasible generalized least square method evaluates the effects. The study concludes that fossil fuel energy consumption follows a U-shaped relationship explaining infant mortality. The results point out that over-consumption of fossil fuel energy is deteriorating the living standards considering low air quality levels in Asian economies.