The primary purpose of this article is to examine the issue of estimating the finite population distribution function from auxiliary information, such as population mean and rank of the auxiliary variables, that are already known. In order to better estimate the distribution function (DF) of a finite population, two improved estimators are developed. The bias and mean squared error of the suggested and existing estimators are derived up to the first order of approximation. To improve the efficiency of an estimators, we compare the suggested estimators with existing counterpart. Based on the numerical outcomes, it is to be noted that the suggested classes of estimators perform well using six actual data sets. The strength and generalization of the suggested estimators are also verified using a simulation analysis. Based on the result of actual data sets and a simulation study, we observe that the suggested estimator outperforms as compared to all existing estimators which is compared in this study.

BACKGROUND: Patient-reported outcome measures (PROMs) and patient-reported experiences measures (PREMs) are crucial for understanding the impact of GD on quality of life and patient\'s perceptions on care, but also to guide decision-making processes. Nevertheless, no specific PREMs in GD have been published, neither PROMs for Spanish GD patients have been developed.
METHODS: Two project coordinators selected key-points to be included in a PROMs/PREMs questionnaire, and the scientific committee and a group of expert patients contributed to the initial draft. Then, 9 meetings with experts were held to discuss controversial points. After, a questionnaire with 103 items regarding symptomatology, aspects of daily life and care experience was developed. Finally, it was conducted a Delphi survey among a multidisciplinary group of experts in GD.
RESULTS: Consensus was reached on 85 out of the 103 items. Recommendations on PROMs and PREMs regarding symptomatology, aspects of daily life and care experience were obtained. Consensus was reached on the importance of considering fatigue, concentration problems, and communication issues in GD patients using 5-step analog scales. Panelists recommended asking GD patients about the impact on social functioning and work/school performance. Finally, consensus was reached on considering care experiences, such as treatment satisfaction, treatment interruptions or transitions and healthcare professionals involved in patient\'s management to perceive patient\'s perceptions.
CONCLUSIONS: This expert consensus may help developing GD-specific PROMs/PREMs for improving GD management. Properly developed and validated PROMs/PREMs may help decision-making, establishing patient-tailored therapeutic and follow-up goals.

BACKGROUND: Patient-reported outcomes (PROs) are emerging as a quality marker for breast cancer care provision. Patient-reported experience (PRE) is equally important, but challenges in qualitative research and documentation have resulted in limited data on oncoplastic breast-conserving surgery (OPBCS). This qualitative study aimed to explore the experiences of patients who underwent OPBCS.
METHODS: Women who underwent OPBCS between 2015 and 2021 at the Breast Unit of Uppsala University Hospital were followed up longitudinally using PROs. All participants were invited to share their experiences beyond PROs through a \"diary.\" Patients\' experiences with care, pre- and postoperatively, and other insights and thoughts that they considered important were documented and analyzed. A narrative research methodology, along with thematic analysis, was employed.
RESULTS: Of the 122 women, 60 (49.2 %) desired to further elaborate on their experiences beyond PROs-related aspects. The most common themes included postoperative side effects, challenges responding to questionnaires, adverse effects of adjuvant treatment, external factors contributing to the preoperative and postoperative overall health status, and satisfaction with the surgery and medical staff. Specifically, 26.7 % of the respondents stated that they felt that PROs were not adequately specific and had further reflections.
CONCLUSIONS: Patients did not report different experiences depending on the operative technique. Their experience focused on the perception of wholeness and potential ambiguities in the PRO questionnaires. Documenting PRE is crucial, as it enables individualized assessment following breast cancer treatment, thereby strengthening patient-centered care.

The problem of estimating the variance of a finite population is an important issue in practical situations where controlling variability is difficult. During experiments conducted in the fields of agriculture and biology, researchers often face this issue, resulting in outcomes that appear uncontrollable for the desired results. Using auxiliary information effectively has the potential to enhance the precision of estimators. This article aims to introduce improved classes of efficient estimators that are specifically designed to estimate the study variable\'s finite population variance. When stratified random sampling is used, these estimators are particularly efficient when the minimum and maximum values of the auxiliary variable are known. The bias and mean squared error (MSE) of the proposed classes of estimators are determined by a first-order approximation. In order to evaluate their performance and verify the theoretical results, we performed simulation research. The proposed estimators show higher percent relative efficiencies ( P R E s ) in all simulation scenarios compared to other existing estimators, according to the results. Three datasets are utilized in the application section, which are used to further validate the effectiveness of the proposed estimators.

The post-transcriptional regulatory element (PRE) is present in all HBV mRNAs and plays a major role in their stability, nuclear export, and enhancement of viral gene expression. Understanding PRE\'s structure, function, and mode of action is essential to leverage its potential as a therapeutic target. A wide range of PRE-based reagents and tools have been developed and assessed in preclinical and clinical settings for therapeutic and biotechnology applications. This manuscript aims to provide a systematic review of the characteristics and mechanism of action of PRE, as well as elucidating its current applications in basic and clinical research. Finally, we discuss the promising opportunities that PRE may provide to antiviral development, viral biology, and potentially beyond.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin\'s lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

The main objective of the current study is to suggest an enhanced family of log ratio-exponential type estimators for population distribution function (DF) using auxiliary information under stratified random sampling. Putting different choices in our suggested generalized class of estimators, we found some Specific estimators. The bias and MSE expressions of the estimators have been approximated up to the first order. By using the actual and simulated data sets, we measured the performance of estimators. Based on the results, the suggested estimators for DF show better performance as compared to the preliminary estimators considered here. The suggested estimators have a advanced efficiency than the other estimators examined with the estimators F‾ˆlogPR(st)2, and F‾ˆlogPR(st)4 for both the actual and simulated data sets. The magnitude of the improvement in efficiency is noteworthy, indicating the superiority of the proposed estimators in terms of MSE.

Estimation of population mean is a determined subject issue in sampling surveys and many efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this, we suggest an improved exponential ratio-cum-ratio estimator using transformed auxiliary variables under ranked set sampling scheme. Theoretical comparison between estimators is made in terms of mean square errors (MSEs), percentage relative efficiencies (PREs), and percentage relative root mean squared error (PRRMSE). The numerical expression for the bias and MSE of the suggested estimator is derived up to first order of approximation. Based on the results of actual data sets and a simulation study, it is found that the suggested estimator perform well as compared to its existing counterparts.

In this article, our main aim is to suggest enhanced families of estimators for estimating the population distribution function (DF) using twofold auxiliary evidence within the framework of simple random sampling. Numerical analysis is performed on four different actual data sets. The precision of the estimators is further investigated exhausting a simulation study. As equated with existing estimators, the suggested families of estimators have minimum mean square error (MSE) and higher percentage relative efficiency (PRE). The succeeding recommended family of estimators outperforms the first family of estimators across all data sets. These are positive indicators of its performance. The theoretical result shows that the recommended family of estimators performs better than the existing estimators. The extent of improvement in efficiency is noteworthy, indicating the superiority of the suggested estimators in terms of minimum MSE.

In this article, we suggest an enhanced estimator for the estimation of finite population variance using twofold auxiliary variable under stratified random sampling. The numerical expressions for the bias and MSE are determined up to the first order of approximation. In order to effectively validate the theoretical findings, three actual data sets are included. Additionally, the application of the suggested estimators is demonstrated using a simulation study. Results of an empirical comparison among the suggested and existing estimators were investigated. To determine how good the suggested estimator, in comparison to the preliminary estimators, the MSE criterion is used. The suggested estimator has a smaller MSE and better PRE than existing estimators, according to numerical results utilizing actual data sets and a simulation analysis.