Analysis Of Real-life Data Research Articles

Time trends of cardiovascular risk management in type 1 diabetes - nationwide analyses of real-life data

BackgroundIndividuals diagnosed with and treated for type 1 diabetes (T1D) have increased risk of micro- and macrovascular disease and excess mortality. Improving cardiovascular (CV) risk factors in individuals with T1D is known to reduce diabetes- related CV complications.AimTo examine time trends in CV risk factor levels and CV-protective treatment patterns. Additionally, examine incidence rates of diabetes-related CV complications in relation to exposure CV-protective treatment.MethodsWe analysed records from 41,630 individuals with T1D, registered anytime between 1996 and 2017 in a nationwide diabetes register. We obtained CV risk factor measurements (2010–2017), CV-protective drug profiles (1996–2017) and CV complication history (1977–2017) from additional nationwide health registers.ResultsFrom 2010 to 2017 there were decreasing levels of HbA1c, LDL-C, and blood pressure. Decreasing proportion of smokers, individuals with glycaemic dysregulation (HbA1c ≥ 58 mmol/mol), dyslipidaemia (LDL-C > 2.6 mmol/l), and hypertension (≥ 140/85 mmHg). Yet, one fifth of the T1D population by January 1st, 2017 was severely dysregulated (HbA1c > 75 mmol/mol). A slight increase in levels of BMI and urinary albumin creatinine ratio and a slight decrease in estimated glomerular filtration rate (eGFR) levels was observed. By January 1st, 2017, one fourth of the T1D population had an eGFR < 60 ml/min/1.73 m2. The proportion of the T1D population redeeming lipid-lowering drugs (LLDs) increased from 5% in 2000 to 30% in 2010 followed by a plateau and then a decline. The proportion of the T1D population redeeming antihypertensive drugs (AHDs) increased from 28% in 1996 to 42% in 2010 followed by a tendency to decline. Use of LLDs was associated with lower incidence of micro- and macrovascular complications, while use of AHDs had higher incidence of CVD and CKD, when compared to non-use and discontinued use, respectively.ConclusionImprovements were seen in CV risk factor control among individuals with T1D in Denmark between 2010 and 2017. However, there is clearly a gap between current clinical guidelines and clinical practice for CV risk management in T1D. Action is needed to push further improvements in CV risk control to reduce CVD and the related excess mortality.

Autologous hematopoietic stem cell transplantation improves long-term survival—data from a national registry

BackgroundCurrent recommendations on the management of systemic sclerosis (SSc) suggest that autologous hematopoietic stem cell therapy (HSCT) can be a rescue therapy for patients with rapidly progressive SSc.ObjectivesTo assess the safety and efficacy of HSCT for patients with SSc and to compare these with non-HSCT patients in a control cohort with adjusted risk factors.MethodsA retrospective analysis of data from the multicentric German network for systemic scleroderma (DNSS) with 5000 patients with SSc. Control groups consisted of all patients with diffuse cutaneous (dc)-SSc (group A) and an adjusted high-risk cohort of male patients with Scl70-positive dc-SSc (group B).ResultsEighty SSc patients received an HSCT 4.1 ± 4.8 years after SSc diagnosis. Among them, 86.3% had dc-SSc, 43.5% were males, and 71.3% were positive for Scl70 antibodies. The control group A (n=1513) showed a significant underrepresentation of these risk factors for mortality. When the survival of the control group B (n=240) was compared with the HSCT group, a lower mortality of the latter was observed instead. Within 5 years after HSCT, we observed an improvement of the mRSS from 17.6 ± 11.5 to 11.0 ± 8.5 (p=0.001) and a stabilization of the DLCO. We did not see differences in transplant-related mortality between patients who received HSCT within 3 years after SSc diagnosis or later.ConclusionOur analysis of real-life data show that the distribution of risk factors for mortality is critical when HSCT cohorts are compared with non-HSCT control groups.

SC38 - Major adverse cardiovascular events (MACE) related to phosphodiesterase 5 inhibitors (PDE5I): analysis of real-life data from eudra-vigilance (EV) database

SC38 - Major adverse cardiovascular events (MACE) related to phosphodiesterase 5 inhibitors (PDE5I): analysis of real-life data from eudra-vigilance (EV) database

THE IMPORTANCE OF RETROSPECTIVE DATA ANALYSIS IN GENETIC COUNSELLING FOR PGT-SR – MATERNAL AGE IS MORE SIGNIFICANT THAN REARRANGEMENT TYPE

Analysis of real-life clinical data to build a robust guide for genetic counselling of PGT-SR patients, including information on the availability of embryos for transfer and cycle outcomes.

Economic Impact, Design, and Significance of Intermodal Freight Distribution in Pakistan

The goal of this study is to quantify and analyse the costs of road-intermodal freight transportation in Pakistan. For the design of the problem, a performance model is utilised, which provides calculations of the costs incurred in road and intermodal freight transports. Our research shows a big cost difference between road and road-rail freight transport. The results show that the cost of road freight transport is 1.48 million Pkr/day and the cost of intermodal freight transport is 1.26 million Pkr/day for the delivery of 220 tonnes of cargo to the consumers. The quantitative analysis of real-life data from one of Asia's leading paper and board businesses used in this study demonstrates operational benefits of more than 60% cost reductions when switching from road to intermodal freight (road-rail) transport. Sensitivity analysis is also presented to assist decision-makers in selecting the most efficient and cost-effective mode of transportation.

A Comparative Analysis of Semiparametric Tests for Fractional Cointegration in Panel Data Models

Several authors have studied fractional cointegration in time series data, but little or no consideration has been extended to panel data settings. Therefore, in this paper, we compare the finite sample behaviour of existing fractional cointegration time-series test procedures in panel data settings. This comparison is performed to determine the best tests that can be adapted to fractional cointegration in panel data settings. Specifically, simulation studies and real-life data analysis were performed to study the changes in the empirical type I error rate and power of six semiparametric fractional cointegration tests in panel settings. The various results revealed the limitations of the tests in the nonstationary and low or high correlation of the residual errors conditions. Also, two of the test procedures were recommended for testing the null hypothesis of no fractional cointegration in both time series and panel data settings.

Bootstrapping Whittle estimators

Summary Fitting parametric models by optimizing frequency-domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the underlying process. This makes the implementation of asymptotic results for the construction of confidence intervals or for assessing the variability of estimators difficult in practice. In this paper we propose a frequency-domain bootstrap method to estimate the distribution of Whittle estimators that is asymptotically valid under assumptions that not only allow for possible model misspecification, but also for weak dependence conditions that are satisfied by a wide range of stationary stochastic processes. Adaptations of the bootstrap procedure developed to incorporate different modifications of Whittle estimators proposed in the literature, such as, for instance, tapered, debiased or boundary extended Whittle estimators, are also considered. Simulations demonstrate the capabilities of the bootstrap method proposed and its good finite sample performance. A real-life data analysis on sunspots is also presented.

Estimation of order restricted location/scale parameters of a general bivariate distribution under general loss function: some unified results

We consider component-wise equivariant estimation of order restricted location/scale parameters of a general bivariate distribution under quite general conditions on underlying distributions and the loss function. This paper unifies various results in the literature dealing with sufficient conditions for finding improvements over arbitrary location/scale equivariant estimators. The usefulness of these results is illustrated through various examples. A simulation study is considered to compare risk performances of various estimators under bivariate normal and independent gamma probability models. A real-life data analysis is also performed to demonstrate applicability of the derived results.

Some properties of weighted survival extropy and its extended measures

We study some properties of weighted survival extropy measure. This is a shift dependent measure and put more weights to the large values of the observed random variable. Then we introduce weighted extended survival extropy and its dynamic version. Various properties of the proposed measures are obtained. Some generalized inequalities related to weighted extended survival extropy are studied. Non-parametric estimators of these measures are proposed and their asymptotic properties are investigated. The performance of the estimators is assessed through a simulation study. Finally, it is shown that weighted survival extropy measure can be used in model discrimination and as an alternative risk measure. Analysis of real life data is considered for illustration.

Local polynomial smoothing based on the Kaplan–Meier estimate

The local polynomial modeling of the Kaplan–Meier estimate for random designs under the right censored data setting is investigated in detail. Two classes of boundary aware estimates are developed: estimates of the distribution function and its derivatives of any arbitrary order and estimates of integrated distribution function derivative products. Their statistical properties are quantified analytically and their implementation is facilitated by the development of corresponding data driven plug-in bandwidth selectors. The asymptotic rate of convergence of the plug-in rule for the estimates of the distribution function and its derivatives is quantified analytically. Numerical evidence is also provided on its finite sample performance. A real life data analysis illustrates how the methodological advances proposed herein help to generate additional insights in comparison to existing methods.

Remote physiology practical: Viable alternative to in-person practical in health sciences education?

Introduction: Practicals are core components of an undergraduate health sciences curriculum to promote experiential learning and motivation in students. With restrictions on traditional forms of face-to-face practicals during the COVID-19 pandemic, we designed and investigated the efficacy of remote practicals as a viable learning strategy in exercise physiology teaching. Methods: Student volunteers were instructed to perform a graded exercise test in a remote setting and provide their collected data for subsequent discussion in an online lecture. The effectiveness of this remote practical in promoting students’ motivation and learning outcomes achievement was assessed via an anonymous questionnaire containing 29 closed-ended and 2 open-ended items. Continual Assessment (CA) results were also investigated. Unpaired student’s t-tests were performed for comparisons between interventions with significance level set at P&lt;0.05. Results: Sixty-one (out of 81; 75%) students responded to the questionnaires and 49 (60%) consented to the use of their CA results for this study. Results revealed that students were moderately motivated and attained strong achievement of learning outcomes. When compared to students who did not volunteer for the hands-on component of the remote practical, students who participated in the hands-on component reported significantly higher self-efficacy (P&lt;0.05) in explaining the practical procedures to their peers. Qualitative analysis further revealed that experiential learning and real-life data analysis were the central reasons supporting the effectiveness of the remote practical. Students were generally satisfied and would recommend the remote practical to future students. Conclusion: Our study highlights the potential of remote practicals as viable alternatives to traditional practicals.

Weighted Least Squares Estimation for AR(1) Model With Incomplete Data

Time series forecasting is the main objective in many life applications such as weather prediction, natural phenomena analysis, financial or economic analysis, etc. In real-life data analysis, missing data can be considered as a feature that the researcher faces because of human error, technical damage, or catastrophic natural phenomena, etc. When one or more observations are missing, it might be urgent to estimate the model as well as to estimate the missing values which lead to a better understanding of the data, and more accurate prediction. Different time series require different effective techniques to have better estimates for those missing values. Traditionally, the missing values are simply replaced by mean and mode imputation, deleted or handled using other methods, which are not convenient enough to address missing values, as those methods can cause bias. One of the most popular models used in estimating time-series data is autoregressive models. Autoregressive models forecast the future values in terms of the previous ones. The first-order autoregressive AR (1) model is the one which the current value is based on the immediately preceding value, then estimating parameters of AR (1) with missing observations is an urgent topic in time series analysis. Many approaches have been developed to address the estimation problems in time series such as ordinary least square (OLS), Yule Walker (YW). Therefore, a suggested method will be introduced to estimate the parameter of the model by using weighted least squares. The properties of the (WLS) estimator are investigated. Moreover, a comparison between those methods using AR (1) model with missing observations is conducted through a Monte Carlo simulation at various sample sizes and different proportions of missing observations, this comparison is conducted in terms of mean square error (MSE) and mean absolute error (MAE). The results of the simulation study state that (WLS) estimator can be considered as the preferable method of estimation. Also, time series real data with missing observations were estimated.

A0203 - Cardiovascular adverse events-related to GnRH agonists and GnRH antagonists: Analysis of real-life data from Eudra-Vigilance database

A0203 - Cardiovascular adverse events-related to GnRH agonists and GnRH antagonists: Analysis of real-life data from Eudra-Vigilance database

Applications of HLMOL-X Family of Distributions to Time Series, Acceptance Sampling and Stress-strength Parameter

In this paper, the applications of the half logistic-Marshall Olkin X family of distributions are investigated with special emphasis to the half logistic-Marshall Olkin Lomax distribution. The specific areas we concentrated are time series modeling, acceptance sampling plan and stress-strength analysis. Different autoregressive minification structures of order one are introduced. The acceptance sampling plan is detailed by considering life time of products following the half logistic-Marshall Olkin Lomax distribution. The stress-strength reliability of the half logistic-Marshall Olkin Lomax distribution is derived and estimated. A simulation study is carried out to examine the bias, mean square error, average confidence length and coverage probability of the maximum likelihood estimator of thestress-strength reliability. Finally a real-life data analysis has also been presented.

Application ProteGo Stop Covid - Could it have been an opportunity to prevent Covid-19 spreading?

This article has several aims. The first one, based on analysis of real-life data of the first Norwegian contact tracing app “The Smittestopp app”, is to show how digital contact tracing apps work and their function and possible impact in COVID-19-crisis. I mainly focused on apps designed to automatically tell users or/and public health officials whether somebody has potentially been exposed to COVID-19. What are the factors that could impact its reception from the public opinion? The second one is to present the result of automated contact tracing apps that are backed by national governments in selected countries. And finally, I would like to look at the social, political, economic, and psychological circumstances of launching the Polish application “ProteGo Stop Covid”. I characterize the process and conditions that occurred when the Polish government was working on it and try to answer the question if it were possible to implement this process more effectively.

A Novel Method of Clustering Using a Stochastic Approach

Cluster analysis of real-life data often encounters the challenges of noisy data or may rely heavily on the uncertainty of the main clustering variable owing to its stochastic nature, which has a potential influence on its performance. In this study, we propose a novel clustering technique that is efficient in dealing with noise and uncertainty in a dataset by adopting a stochastic approach that uses realistic values of data points by assuming a continuous probability distribution instead of exact values. By estimating the best-fit probability distribution of the clustering variable, the proposed method formulates the problem of determining the most homogeneous clusters by determining the optimum cluster partitions (OCP) as a mathematical programming problem (MPP). A computer-intensive dynamic programming technique was used to solve the MPP and determine the OCP, which minimized the sum of the weighted intracluster standard deviations. The proposed technique is then demonstrated in this study using univariate data that follows a normal distribution, which is a symmetric distribution, as well as the Weibull distribution, which is a skewed distribution. Numerical examples were also presented to illustrate the computational details of the proposed method. Finally, using both simulated and real datasets, a comparative analysis of the effectiveness of the proposed technique was performed against four advanced clustering methods: k-means, fuzzy c-means, expectation-maximization, and Genie++ hierarchical clustering. The results reveal that the proposed method works well and produces more efficient clusters than other methods.

Modeling serially correlated heavy-tailed data with some missing response values using stochastic EM algorithm

The linear regression model is a popular tool used by almost all in different areas of research. The model relies mainly on the assumption of uncorrelated errors from a Gaussian distribution. However, many datasets in practice violate this basic assumption, making inference in such cases invalid. Therefore, the linear regression model with structured errors driven by heavy-tailed innovations are preferred in practice. Another issue that occur frequently with real-life data is missing values, due to some reasons such as system breakdown and labor unrest. Despite the challenge these two issues pose to practitioners, there is scarcity of literature where they have jointly been studied. Hence, this article considers these two issues jointly, for the first time, and develops an efficient parameter estimation procedure for Student’s-t autoregressive regression model for time series with missing values of the response variable. The procedure is based on a stochastic approximation expectation–maximization algorithm coupled with a Markov chain Monte Carlo technique. The procedure gives efficient closed-form expressions for the parameters of the model, which are very easy to compute. Simulations and real-life data analysis show that the method is efficient for use with incomplete time series data.

Treatment of Cutaneous Squamous Cell Carcinoma with Immune Checkpoint Inhibitors in Special Populations

Cutaneous squamous cell carcinoma (cSCC) may develop in patients with dysregulated immune activation (pre-existing autoimmune diseases or immunosuppression due to hematopoietic/solid organ transplant recipients), patients with a compromised immune function (long-term immunosuppression), and patients carrying chronic viral infections, or those affected by lymphoproliferative diseases. It should be also considered that patients presenting with immunosuppression have a high incidence of cSCC (65-250-times higher than general population), highlighting the central role played by the immune system in the development of cSCC. All these cases must be considered as "special populations" for treatment with immune checkpoint inhibitors (ICIs), as the safety and activity of these drugs have not been studied on these specific cases, since these patients were excluded from clinical trials leading to approval of ICIs. It is therefore important to gain as much information as possible from the analysis of real-life data, to derive an indication to be adopted in everyday clinical setting. Moreover, therapeutic alternatives other than ICIs are scarce, mainly consisting in chemotherapy and anti-EGFR agents, whose activity is lower than immunotherapy and whose toxicity (particularly with chemotherapy) are not sustainable by this frail population. Here, we describe the current evidence of treatment with ICIs in special populations and conclude that it is necessary to find a balance between treatment risks (toxicities) and benefits (efficacy), as well as engaging a multidisciplinary team of experts to thoroughly manage and treat these patients.

Sustained Residual Disease Negativity Assessed By Diffusion-Weighted Whole-Body MRI (DW-MRI) Has Strong Predictive Relevance for Survival in Newly Diagnosed Multiple Myeloma Patients on Maintenance Therapy after Autologous Stem Cell Transplantation (ASCT)

Sustained Residual Disease Negativity Assessed By Diffusion-Weighted Whole-Body MRI (DW-MRI) Has Strong Predictive Relevance for Survival in Newly Diagnosed Multiple Myeloma Patients on Maintenance Therapy after Autologous Stem Cell Transplantation (ASCT)

SC37 - Sexual adverse events-related to dutasteride e finasteride: analysis of real-life data from Eudra-Vigilance database

SC37 - Sexual adverse events-related to dutasteride e finasteride: analysis of real-life data from Eudra-Vigilance database