Analysis Of Real-life Data Research Articles

Adjustable compression wraps: back to the future

Adjustable Compression Wraps (ACWs) are used to treat a variety of conditions, in particular venous leg ulcers, edema and lymphedema. Publications on the use of these devices are ultimately few and far between, with limitations or biases (small sample sizes, methodological weaknesses, non-comparative studies, lack of interface pressures). A development model like that used in the study of a drug could be more rational. We propose five phases: phase 1) discovery and development, phase 2) in vitro testing, phase 3) clinical trials, phase 4) real-life data analysis, medico-economic investigation using Artificial Intelligence (AI), and phase 5) preparation of regulatory dossiers. This lack of coherence in development could be an obstacle to widespread clinical use. Using AI to analyse large quantities of data could reduce the cost and number of clinical trials needed to generate more robust evidence. This would make it possible to better define the clinical effects of ACWs in specific situations (e.g., leg wounds in the elderly, diabetic wounds, mixed leg ulcers, secondary lymphedema of the upper limb, efficacy, and comfort...) and to compare treatment costs between bandages and ACWs. If we don't explore the future with AI, we'll never know what will be useful for the present - hence the title “Back to the Future”.

P206 How is the efficacy of patient selection criteria for adjuvant pembrolizumab after curative surgical treatment of clear cell renal cell carcinoma with intermediate to high risk of recurrence? A real-life data analysis from single institutional experience

P206 How is the efficacy of patient selection criteria for adjuvant pembrolizumab after curative surgical treatment of clear cell renal cell carcinoma with intermediate to high risk of recurrence? A real-life data analysis from single institutional experience

Bayesian estimation strategy for multi-component geometric life testing model under doubly type-1 censoring scheme

This study develops a Bayesian approach for estimating the unknown parameters of the 3-component mixture of geometric (3-CMG) model under a doubly type-I censoring scheme (DT1CS). The derivations of the Bayes estimators (BEs) and Bayes risks (BRs) are presented under square error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) using Beta prior under DT1CS. The strategy is evaluated through extensive simulation and real-life data analysis, showing the strength and efficiency of the newly proposed model. The study recommends that the SELF is the optimal choice for accurately estimating the unknown parameters of the 3-CMG model.

Estimation of mean using under-reported and overdispersed count data

Count data arising in various fields of applications are often under-reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. Further, overdispersion in count data may arise due to inherent heterogeneity of the data. Negative Binomial distribution is a viable candidate for modeling overdispersed count data. However, in presence of undercount the negative binomial model needs to be adjusted. In this paper, we shall develop likelihood-based methodologies for estimation of mean using validation data after accounting for underreporting and overdispersion. The impact of ignoring undercount on the coverage and length of the confidence intervals is investigated using extensive numerical studies. The study is supplemented with a real-life data analysis.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

Improving Understanding of Data Processing Concepts through the TARL Approach (Teaching at the Right Level) in Class 5 Students of SD Muhammadiyah 23 Surakarta

This study aims to enhance the understanding of data processing concepts among fifth-grade students at SD Muhammadiyah 23 Surakarta through the Teaching at the Right Level (TaRL) approach. The research was conducted using a Classroom Action Research (CAR) methodology, which involved two cycles, each consisting of two meetings. The primary issue identified was the low level of students' comprehension of data processing concepts, such as data collection, classification, interpretation, and the use of diagrams and tables. To address this, an active and contextual learning approach was implemented, involving real-life data collection and analysis, the use of digital tools for data visualization, and Problem-Based Learning (PBL) strategies.In the first cycle, the intervention revealed limited improvement in student engagement and learning outcomes. Consequently, the second cycle introduced revised group formations and additional instructional strategies, including ice-breaking activities. The results showed a significant improvement in student participation and understanding, with an increase in average class scores from 65 in the first cycle to 81 in the second cycle. The percentage of students meeting the learning criteria rose from 14.28% to 82.14%. This study concludes that applying TaRL, along with improved group dynamics and interactive learning methods, effectively enhances students' data processing skills and overall learning outcomes.

Treatment outcomes and their predictors in children hospitalized with varicella complicated by bacterial superinfections after pandemic of COVID-19 – a retrospective multicenter analysis of real-life data in Poland

Treatment outcomes and their predictors in children hospitalized with varicella complicated by bacterial superinfections after pandemic of COVID-19 – a retrospective multicenter analysis of real-life data in Poland

Psychopathological variables in chronic migraine patients with different therapeutic approach: psychological profile differences and impact on therapeutic efficacy in real life.

Migraine is a debilitating neurological condition linked to various psychological comorbidities. The aims of our study are: 1) to evaluate potential psychopathological differences between patients in treatment with anti-CGRP monoclonal antibodies (SPECIFIC group) and those with onabotulinumtoxin-A/oral pharmacotherapy (Group NON-SPECIFIC), 2) to compare treatment efficacy over time between groups, and examined whether psychopathological comorbidities can influence it. This is a post-hoc ambispective study: a retrospective analysis of patient-level real-life data prospectively collected for clinical evaluation. We enrolled 102 patients with chronic migraine (CM), 64 in treatment with erenumab or galcanezumab, and 38 with botulinum toxin or oral pharmacotherapies. Psychopathological variables are assessed at baseline, whereas clinical factors over time. The NON-SPECIFIC group showed more pronounced emotion regulation difficulties (DERS, p = 0.001), alexithymia (TAS-20, p = 0.012), and impulsiveness (BIS-11, p = 0.002) with respect to the SPECIFIC group. Moreover, treatment efficacy overtime was more pronounced in the group with anti-CGRP treatment with a reduction of migraine frequency overtime, pain intensity, and improved quality of life up to six months post-treatment. Psychopathological comorbidity did not influence treatment efficacy. Our study highlighted more pronounced psychopathological comorbidities in patients in treatment with NON-SPECIFIC therapies in terms of impulsivity, alexithymia, and emotion regulation. Moreover, treatment efficacy overtime was more pronounced and stable over time in the SPECIFIC group, and psychopathological comorbidity did not influence it.

Likelihood-based inference for semi-parametric transformation cure models with interval censored data

A simple yet effective way of modeling survival data with cure fraction is by considering Box-Cox transformation cure model (BCTM) that unifies mixture and promotion time cure models. In this article, we numerically study the statistical properties of the BCTM when applied to interval censored data. Time-to-events associated with susceptible subjects are modeled through proportional hazards structure that allows for non-homogeneity across subjects, where the baseline hazard function is estimated by distribution-free piecewise linear function with varied degrees of non-parametricity. Due to missing cured statuses for right censored subjects, maximum likelihood estimates of model parameters are obtained by developing an expectation-maximization (EM) algorithm. Under the EM framework, the conditional expectation of the complete data log-likelihood function is maximized by considering all parameters (including the Box-Cox transformation parameter α) simultaneously, in contrast to conventional profile-likelihood technique of estimating α. The robustness and accuracy of the model and estimation method are established through a detailed simulation study under various parameter settings, and an analysis of real-life data obtained from a smoking cessation study.

#565 Renal involvement in solid cancers: epidemiological, clinical and histological characteristics study of 154 onconephrology patients

Abstract Background and Aims Onconephrology is a growing discipline that aims to improve the management of patients with cancer and kidney disease. If renal histology is an essential key, the anatomopathological data remain weak. We propose an analysis of real-life data from patients managed in onconephrology consultations and had renal biopsy. Method Patients with active cancer who had a renal biopsy (RB) between 2015 and 2020 were included, and their clinicobiological and histological data were retrospectively analyzed. Our cohort consisted of 154 patients (83 women) with a mean age of 58 years who went for onconephrology care. Results One hundred twelve patients presented with proteinuria, 95 with acute renal failure, and 59 with arterial hypertension. Histologically, interstitial fibrosis was found in 74% of RBs, tubular atrophy in 55.1%, arteriolar hyalinosis in 58.4%, and fibrous endarteritis in 54.4%. Regarding the main acute lesions, thrombotic microangiopathy (TMA) was found in 29.9% of biopsies, acute tubular necrosis (ATN) in 51.3%, and acute interstitial nephritis in 24.8%. The etiological diagnosis most often made was the nephrotoxicity of anticancer drugs (87 patients), followed by a functional cause (15 patients) and renal disease unrelated to cancer (13 patients). Sixty-seven patients presented with at least 2 associated diagnoses. Different clusters were found, highlighting that anti-PD1 and anti-VEGF therapy were the most commonly involved drugs. Conclusion The nephrotoxicity of anticancer drugs was the etiology most often determined following RB this a onconephrology population, predominantly with diagnoses of TMA and ATN. Chronic histological lesions were very common. The etiological diagnoses were sometimes mixed, and primary or paraneoplastic glomerulopathies were detected mainly through histology.

A new over-dispersed count model based on Poisson-Geometric convolution

A new two-parameter discrete distribution, namely the PoiG distribution, is derived by the convolution of a Poisson variate and an independently distributed geometric random variable. This distribution generalizes both the Poisson and geometric distributions and can be used for modeling over-dispersed as well as equi-dispersed count data. A number of important statistical properties of the proposed count model, such as the probability generating function, the moment generating function, the moments, the survival function and the hazard rate function have been studied in detail. Monotonic properties such as the log concavity are also studied. Stochastic ordering for the proposed model is also investigated in detail. Method of moments and the maximum-likelihood estimators of the parameters of the proposed model are found to work well in simulation experiments as well as in the real-life data analysis. Asymptotic confidence intervals and the coverage probabilities of the parameters are also studied through extensive simulation experiments. Two real life modelling applications demonstrate that the proposed distribution may prove to be useful for the practitioners for modelling over-dispersed count data compared to its closest competitors.

Major adverse cardiovascular events related to phosphodiesterase 5 inhibitors: analysis of real-life data from Eudra-Vigilance database.

Phosphodiesterase 5 inhibitors (PDE5i) are the standard medical treatment for erectile dysfunction. Aim of our study was to evaluate the rate of major adverse cardiovascular events (MACE) reported during PDE5i treatment based on Eudra-Vigilance (EV) reports. EV database is the system for managing and analyzing data on suspected adverse reactions to medicines which have been authorized or being studied in clinical trials in the European Economic Area. MACE are defined as non-fatal stroke, non-fatal myocardial infarction, non-fatal congestive heart failure, revascularization after aorto-coronary graft bypass and cardiovascular death. We recorded the number of MACE for sildenafil, tadalafil, vardenafil, avanafil per category and severity until 1st July 2023. Pooled Relative Risk (PRR) was used to compare data between drugs. Overall, 951 MACE events were reported. Most of them were observed in younger patients <65 years old (452/951 events, 48%). Overall, 377/8939 (4%) MACE events were observed for sildenafil, 221/5213 (4%) for tadalafil, 50/1029 (4%) for vardenafil and no events for avanafil. No significative differences were reported comparing sildenafil and tadalafil (PRR 0.71-0.99, IQR 0.61-1.35, P>0.05), neither sildenafil vs. vardenafil (PRR 0.68-0.79, IQR 0.43-1.55, P>0.05), neither tadalafil vs. vardenafil (PRR 0.77-0.95, IQR 0.64-1.30. P>0.05) even when compared for age. Comparison between different classes of age showed MACE were more frequent in patients younger than 65 years old taking sildenafil and tadalafil when compared to patients older than 85 years old (PRR 0.02-0.11. IQR 0.01-0.40. P<0.01) and when compared to patients in 65-85 class of age (PRR 0.02-0.12, IQR 0.01-0.95, P<0.01). Real life data is consistent with MACE related to PDE5i. PDE5is are infrequently (<5%) associated with MACE. However, risk seems higher in younger patients, particularly for sildenafil (452/951 events, 48%). Clinicians should consider these data when prescribing PDE5i especially in young patients.

56P Prevalence, clinical characteristics, and treatment outcomes of patients with KRAS-mutated non-squamous NSCLC and PD-L1 expression: Real-life data analysis

56P Prevalence, clinical characteristics, and treatment outcomes of patients with KRAS-mutated non-squamous NSCLC and PD-L1 expression: Real-life data analysis

Exploring future perspectives and pipeline progression in vaccine research and development.

The COVID-19 pandemic had a profound impact on vaccines' Research and Development, on vaccines' market, and on immunization programmes and policies. The need to promptly respond to the health emergency boostered resources' al-location and innovation, while new technologies were made available. Regulatory procedures were revised and expedited, and global production and distribution capacities significantly increased. Aim of this review is to outline the trajectory of research in vaccinology and vaccines' pipeline, highlighting major challenges and opportunities, and projecting future perspectives in vaccine preventables diseases' prevention and control. Narrative review. We comprehensively consulted key biomedical databases including "Medline" and "Embase", preprint platforms, including"MedRxiv" and "BioRxiv", clinical trial registries, selected grey literature sources and scientific reports. Further data and insights were collected from experts in the field. We first reflect on the impact that the COVID-19 had on vaccines' Research and Development, regulatory frameworks, and market, we then present updated figures of vaccines pipeline, by different technologies, comparatively highlighting advantages and disadvantages. We conclude summarizing future perspectives in vaccines' development and immunizations strategies, outlining key challenges, knowledge gaps and opportunities for prevention strategies. COVID-19 vaccines' development has been largely supported by public funding. New technologies and expetited autho-rization and distribution processes allowed to control the pandemic, leading vaccines' market to grow exponentially. In the post-pandemic era investments in prevention are projected to decrease but advancements in technology offer great potential to future immunization strategies. As of 2023, the vaccine pipeline include almost 1,000 candidates, at different Research and Development phase, including innovative recombinant protein vaccines, nucleic acid vaccines and viral vector vaccines. Vaccines' technology platforms development varies by disease. Overall, vaccinology is progressing towards increasingly safe and effective products that are easily manufacturable and swiftly convertible. Vaccine research is rapidly evolving, emerging technologies and new immunization models offer public health new tools and large potential to fight vaccines preventables diseases, with promising new platforms and broadened target populations. Real-life data analysis and operational research is needed to evaluate how such potential is exploited in public health practice to improve population health.

Do Double-Expressor High-Grade B-Cell Lymphomas Really Need Intensified Treatment? A Report from the Real-Life Series of High-Grade B-Cell Lymphomas Treated with Different Therapeutic Protocols at the Institute of Oncology Ljubljana.

High-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements are known for their aggressive clinical course and so are the ones with MYC and BCL2 protein overexpression. The optimal therapy for these lymphomas remains to be elucidated. A retrospective analysis of all diffuse large B-cell lymphomas and high-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements diagnosed between 2017 and 2021 at the Institute of Oncology Ljubljana, Slovenia, has been performed. Only patients with double-expressor lymphoma (DEL), double-hit lymphoma (DHL), or triple-hit lymphoma (THL) were included. Demographic and clinical parameters were assessed, as well as progression-free survival (PFS) and overall survival (OS). In total, 161 cases out of 309 (161/309; 52,1%) were classified as DEL. Sixteen patients had DHL, MYC/BCL2 rearrangement was observed in eleven patients, and MYC/BCL6 rearrangement was observed in five patients. Five patients were diagnosed with THL. Out of 154 patients (according to inclusion/exclusion criteria) included in further evaluation, one-hundred and thirty-five patients had double-expressor lymphoma (DEL), sixteen patients had DHL, and three patients had THL. In total, 169 patients were treated with R-CHOP, 10 with R-CHOP and intermediate-dose methotrexate, 19 with R-DA-EPOCH, and 16 with other regimens. The median follow-up was 22 months. The 5-year OS for the whole DEL group was 57.1% (95% CI 45.9-68.3%) and the 5-year PFS was 76.5% (95% CI 72.6-80.4%). The log-rank test disclosed no differences in survival between treatment groups (p = 0.712) while the high-risk international prognostic index (IPI) carried a significantly higher risk of death (HR 7.68, 95% CI 2.32-25.49, p = 0.001). The 5-year OS for DHL patients was 32.4% (95% CI 16.6-48.2%) while all three TH patients were deceased or lost to follow-up. Our analyses of real-life data disclose that the R-CHOP protocol with CNS prophylaxis is a successful and curative treatment for a substantial proportion of DEL patients.

Gini index based goodness-of-fit test for the Lindley distribution

In survival analysis and reliability studies the Lindley distribution has been widely used. In this article, we introduce a new goodness of fit test for the Lindley distribution based on an estimate of the Gini index. The asymptotic distribution of the new test statistic is investigated. The unknown parameter of the Lindley distribution is estimated by the maximum likelihood estimator. The critical points and the actual sizes of the test are obtained by Monte Carlo simulations. A power comparison study of the existing tests is performed. It is shown that the Gini index-based test that we proposed performs well by comparing its powers with that of other tests. A real-life data analysis is finally presented for illustrative purpose.

Automated detection of edge clusters via an overfitted mixture prior

AbstractMost community detection methods focus on clustering actors with common features in a network. However, clustering edges offers a more intuitive way to understand the network structure in many real-life applications. Among the existing methods for network edge clustering, the majority are algorithmic, with the exception of the latent space edge clustering (LSEC) model proposed by Sewell (Journal of Computational and Graphical Statistics, 30(2), 390–405, 2021). LSEC was shown to have good performance in simulation and real-life data analysis, but fitting this model requires prior knowledge of the number of clusters and latent dimensions, which are often unknown to researchers. Within a Bayesian framework, we propose an extension to the LSEC model using a sparse finite mixture prior that supports automated selection of the number of clusters. We refer to our proposed approach as the automated LSEC or aLSEC. We develop a variational Bayes generalized expectation-maximization approach and a Hamiltonian Monte Carlo-within Gibbs algorithm for estimation. Our simulation study showed that aLSEC reduced run time by 10 to over 100 times compared to LSEC. Like LSEC, aLSEC maintains a computational cost that grows linearly with the number of actors in a network, making it scalable to large sparse networks. We developed the R package aLSEC which implements the proposed methodology.

Home management of Covid-19 pneumonia in the early phases of the pandemic: analysis of real-life data of General Practitioners in the Province of Modena from the MAGMA study.

Primary care management of Covid-19 pneumonia in the Province of Modena in the early phases of the pandemic: data integration from MAGMA study. Retrospective study on patients affected of Covid-19 and followed by General Practitioner from March 2020 to April 2021. 5340 patients were studied, 27% of them developed pneumoniae. Among these, most of them were managed entirely at home with an elevated intensity of care. Daily remote monitoring and home visits, together with a personalized pharmacological treatment, especially for the most severe forms, appeared to be the most effective interventions in reducing hospitalizations.

A weighted Gompertz-G family of distributions for reliability and lifetime data analysis

This article is set to push new boundaries with leading-edge innovations in statistical distribution for generating up-to-the-minute contemporary distributions by a mixture of the second record value of the Gompertz distribution and the classical Gompertz model (weighted Gompertz model) using T-X characterization, especially used for two-sided schemes that provide an accurate model. The quantile, ordinary, and complete moments, order statistics, probability, and moments generating functions, entropies, probability weighted moments, Lin’s condition random variable, reliability in multicomponent stress strength system, reversed, and moments of residuals life and other reliability characteristics in engineering, actuarial, economics, and environmental technology were derived in their closed form. To investigate and test the flexibility, viability, tractability, and performance of the proposed Weighted Gompertz-G (WGG) generated model, the shapes of some sub-models of the WGG model were examined. The shapes of the sub-models indicated J-shapes, increasing, decreasing, and bathtub hazard rate functions. The maximum likelihood estimation of the WGG-generated model parameters was examined. An illustration with simulation and real-life data analysis indicated that the WGG-generated model provides consistently better goodness-of-fit statistics than some competitive models in the literature.

Phase II control charts for monitoring the depth-ratio of ball-bearings involving three normal variables

This paper investigates the problem of monitoring the ratio involving three variables, jointly distributed as trivariate normal. The Shewhart-type and two exponentially weighted moving average (EWMA) type schemes for monitoring depth ratio are proposed. The ratio of a normal variable to the average of two other normal variables has wide applications in natural science, production, and engineering. It is defined with slightly different terminology in various contexts, such as depth or aspect ratios. In modern bearing manufacturing, the aspect ratio of width to the average of inner and outer diameters can be an essential indicator of product quality and process stability. While there are many helpful existing charts for monitoring the three components separately or jointly when these characteristics follow a normal distribution, the ratio aspect is often ignored. The Shewhart-type schemes' exact and approximated control limits are considered and analyzed. Numerical results based on Monte-Carlo are conducted using the average run length as a metric with different values of in-control ratio and correlation between the three variables. An application based on the parts manufacturing data illustrates the implementation design of the two control charts. The real-life data analysis shows the efficacy of the proposed monitoring schemes in practice.