Analysis Of Real-life Data Research Articles

Antifungal Combination Therapy in Children with Cancer-A 4-Year Analysis of Real-Life Data of Two Major Pediatric Cancer Centers.

Clinical data on antifungal combination therapy are limited, in particular in the pediatric setting. We analyzed real-life data collected in two major pediatric cancer centers over a period of 4 years. Patients were identified in an observational study on children with acute leukemia and lymphoma or undergoing hematopoietic cell transplantation. Out of 438 patients, 19 patients received 21 episodes of antifungal combination therapy. Therapy was mostly started for sepsis (n = 5) or clinical deterioration with pulmonary infiltrates (n = 10), and less often for periorbital swelling with suspected mold infection (n = 2), clinical deterioration and new skin lesions, secondary antifungal prophylaxis, a persistently elevated galactomannan index, or as pre-emptive treatment (n = 1 each). Diagnostics revealed proven, probable, and possible invasive fungal disease in two, seven and four episodes, respectively. Most regimens included caspofungin (n = 19), and treatment was initiated as first line therapy in 10 episodes. The median duration was 13 days (4–46 days). Nine of the 13 patients with proven, probable, or possible invasive fungal disease survived, which was comparable to patients receiving antifungal monotherapy. Our analysis demonstrates that combination therapy has mainly been prescribed in selected immunocompromised patients with clinical deterioration due to suspected invasive fungal disease or those with sepsis, and is well tolerated. Future studies need to better characterize clinical settings in which patients may benefit from antifungal combination therapy.

A new extended log-Weibull regression: Simulations and applications

The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory. Among well-established generalized (G) classes for flexible modeling, the exponentiated-G, Marshall-Olkin-G and odd log-logistic-G families offer induction of one additional parameter while the beta-G and Kumaraswamy-G classes offer two extra shape parameters. The Marshall-Olkin-odd-loglogistic-G (MOOLL-G) family serves as an alternative to the beta-G and Kumaraswamy-G classes. A new motivation for the MOOLL-G family for competing risk scenarios, some useful properties, and parameter estimation are addressed. The new log-MOOLL-Weibull regression is useful for analysis of real life data. The accuracy of the estimates and the residuals is addressed via Monte Carlo simulations. The presented models outperform some other well-known models.

P0017 - Adverse events related to different antimuscarinics and beta-3-agonist: Analysis of real-life data from Eudra-Vigilance

P0017 - Adverse events related to different antimuscarinics and beta-3-agonist: Analysis of real-life data from Eudra-Vigilance

P0033 - Sexual adverse events-related to dutasteride e finasteride: Analysis of real-life data from EudraVigilance database

P0033 - Sexual adverse events-related to dutasteride e finasteride: Analysis of real-life data from EudraVigilance database

P0034 - Alpha-blockers adverse events: Analysis of real-life data from Eudra-Vigilance

P0034 - Alpha-blockers adverse events: Analysis of real-life data from Eudra-Vigilance

Bayesian meta-elliptical multivariate regression models with fixed marginals on unit intervals

In this paper, we make use of meta-elliptical copula functions to build a new multivariate distribution with fixed marginal distributions and dependence structure to analyze bounded data. Specifically, we present a flexible p-elliptical multivariate probability distribution in the hypercube p with fixed marginal GF-quantile distributions. We then present some illustrative examples and a meta-elliptical multivariate regression model as a flexible alternative to the multivariate normal regression model on unit intervals. A simulation study and real-life data analysis using a Bayesian framework with the extreme-value quantile functions show the flexibility of the proposed meta-multivariate normal regression model for modeling the observed proportion response variables.

Robustness and Model Selection in Configurational Causal Modeling

In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal structures, rendering it impossible to determine exactly how they influence the correctness of selected models. This article develops a computable criterion of fit-robustness, which quantifies the degree to which a CCM model agrees with other models inferred from the same data under systematically varied threshold settings of fit parameters. Based on two extended series of inverse search trials on data simulated from known causal structures, the article moreover provides a precise assessment of the degree to which fit-robustness scoring is conducive to finding a correct causal model and how it compares to other approaches of model selection.

Semiparametric zero-inflated Bernoulli regression with applications

When the observed proportion of zeros in a data set consisting of binary outcome data is larger than expected under a regular logistic regression model, it is frequently suggested to use a zero-inflated Bernoulli (ZIB) regression model. A spline-based ZIB regression model is proposed to describe the potentially nonlinear effect of a continuous covariate. A spline is used to approximate the unknown smooth function. Under the smoothness condition, the spline estimator of the unknown smooth function is uniformly consistent, and the regression parameter estimators are asymptotically normally distributed. We propose an easily implemented and consistent estimation method for the variances of the regression parameter estimators. Extensive simulations are conducted to investigate the finite-sample performance of the proposed method. A real-life data set is used to illustrate the practical use of the proposed methodology. The real-life data analysis indicates that the prediction performance of the proposed semiparametric ZIB regression model is better compared to the parametric ZIB regression model.

Multivariate distributions of correlated binary variables generated by pair-copulas

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions in two and three dimensions with numerical examples. For higher dimensions, we provide a method of constructing a multidimensional binary distribution with specified marginals and equicorrelated correlation matrix. We present a real-life data analysis to illustrate the application of our results.

Consensus guidelines for botulinum toxin therapy: general algorithms and dosing tables for dystonia and spasticity

Botulinum toxin (BT) therapy is a complex and highly individualised therapy defined by treatment algorithms and injection schemes describing its target muscles and their dosing. Various consensus guidelines have tried to standardise and to improve BT therapy. We wanted to update and improve consensus guidelines by: (1) Acknowledging recent advances of treatment algorithms. (2) Basing dosing tables on statistical analyses of real-life treatment data of 1831 BT injections in 36 different target muscles in 420 dystonia patients and 1593 BT injections in 31 different target muscles in 240 spasticity patients. (3) Providing more detailed dosing data including typical doses, dose variabilities, and dosing limits. (4) Including total doses and target muscle selections for typical clinical entities thus adapting dosing to different aetiologies and pathophysiologies. (5) In addition, providing a brief and concise review of the clinical entity treated together with general principles of its BT therapy. For this, we collaborated with IAB—Interdisciplinary Working Group for Movement Disorders which invited an international panel of experts for the support.

A Model of Reliability Optimization of a Stand-Alone Electric Power System with Constraints on Dynamic Stability of the Wind Turbine

The relevance of the study is due to the development herein of a model for reliability optimization of stand-alone power systems with wind turbines and electrochemical power storage devices, with special emphasis within this model put on the specifics of power equipment operation. The key feature of the model developed is that it enables us to factor in the requirements to be met by the equipment as arising from the considerations of dynamic stability of the stand-alone system. When simulating battery storage operating modes, the charge-discharge limits as well as the remaining charge in the storage are taken into account. Thus, the reduction of the total number of considered mixes of the equipment being commissioned is achieved, the computational efficiency of the reliability optimization method is increased, while the validity of modeling results is improved. Development of methods for optimization of reliability of stand-alone electric power systems with wind turbine installations and electrochemical power storage devices while meeting requirements for electrodynamic stability. A stand-alone power system that is assumed to be located in the coastal area of Lake Baikal in the Kabansky State Nature Reserve, Republic of Buryatia, Russia, serves as the object of the study. Calculations are based on multiple simulation of modes of operation of the electric power system by means of the Monte Carlo method. The values of random variables are modeled as per specified laws of distribution and fault rate indicators of power equipment. Modeling of power generation at wind turbines is based on a detailed analysis of real-life weather data (average hourly wind speed, air density and humidity). The method of reliability optimization of stand-alone power systems with wind turbines and electrochemical energy storage devices was developed so as to take into account the requirements to be met by electric power equipment in terms of dynamic stability. The optimization criterion is the minimum expected value of the cost of produced electricity. Power redundanct and energy storage devices are used as means of reliability assurance. The results of calculations attest to the fact that for the natural and climatic zone under consideration, the use of vertical axis wind turbines in a stand-alone power system proves more efficient than the use of horizontal axis wind turbines

Probability of a ship becoming beset in ice along the Northern Sea Route – A Bayesian analysis of real-life data

Ships operating in ice-infested Arctic waters are exposed to a range of ship-ice interaction related hazards. One of the most dangerous of these is the possibility of a ship becoming beset in ice, meaning that a ship is surrounded by ice preventing it from maneuvering under its own power. Such a besetting event may not only result in severe operational disruption, but also expose a ship to severe ice loading or cause it to drift towards shallow water. This may cause significant structural damage to a ship and potentially jeopardize its safety. To support safe and sustainable Arctic shipping operations, this article presents a probabilistic approach to assess the probability of a ship becoming beset in ice. To this end, the proposed approach combines different types of data, including Automatic Identification System (AIS) data, satellite ice data, as well as data on real-life ship besetting events. Based on this data, using a hierarchical Bayesian model, the proposed approach calculates the probability of a besetting event as a function of the Polar Ship Category of a ship, sea area, and the distance travelled in the prevailing ice concentration. The utility of the proposed approach, e.g. in supporting spatiotemporal risk assessments of Arctic shipping activities as well as Arctic voyage planning, is demonstrated through a case study in which the approach is applied to ships operating in the Northern Sea Route (NSR) area. The outcomes of the case study indicate that the probability of besetting is strongly dependent on the Polar Ship Category of a ship and that the probability increases significantly with higher ice concentrations. The sea area, on the other hand, does not appear to significantly affect the probability of besetting.

Non-medical switching from reference to biosimilar etanercept - no evidence for nocebo effect: a retrospective analysis of real-life data.

To evaluate the effectiveness and safety of non-medical switching from reference to biosimilar etanercept in adult patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA) using different information strategies before switching. Data of adult patients with RA, PsA or axSpA who had received reference etanercept were retrospectively analysed. Whether or not patients were informed about the switch from reference to biosimilar etanercept was left to the discretion of the treating rheumatologist. Disease activity and function were regularly assessed in two consecutive visits (week 12 and 24). The scores documented at week 12 week after the switch were defined as primary outcome. Adverse drug events (ADE) were documented. Data of 84 patients were available (44 RA, 25 axSpA and 15 PsA patients), of whom 24 had been informed about the planned switch (28.5%). The scores at week 12 of disease activity and function remained rather unchanged. Neither outcomes nor frequency of ADE were influenced by information strategy. The retention rate was high (96.4% at week 12, 87.6% at week 24). Seven patients were lost to follow-up, and six patients discontinued due to inefficacy or ADE. 18 ADEs were reported in 10 patients (12%). In 3 patients (3.6%) who had 5 ADEs in the first 12 weeks the reference etanercept was successfully readministered. Systematic switch from reference to biosimilar etanercept was not associated with changes in disease activity or function in. This was independent of information on the switch transmitted to the patients.

Migraine in the real-life clinical practice: results of the observational study of diagnosis and treatment patterns in the urban population of the Russian Federation

Analysis of real-life clinical practice data regarding the patterns of diagnosis and treatment of migraine in the Russian Federation. An observational real-world cohort study was conducted from January 2013 to December 2018 (over a 5-year period) to analyze the electronic database of 5 large private clinic chains in Moscow and St. Petersburg. Among 1.15 million people who sought medical care, «G43 Migraine» diagnosis was established in 6.650 people, which accounted for 0.6%. A total of 54% of patients were diagnosed by a neurologist, while 35% by a GP. In roughly half the cases, to diagnose migraine, the doctors had to resort to additional methods of examination. While 53% of patients had indications for preventive treatment, it was prescribed only to 12% of those. Of these, the treatment strategy chosen for 29% of patients was changed: either the intragroup/intergroup switching of the medicinal product took place, or the combined treatment was prescribed. In the real-life outpatient clinical practice, the level of diagnosis of migraine is extremely low. Additional methods of examination are excessively used for the diagnosis of migraine. The insufficient use of preventive medication and common use of therapies with unsubstantiated efficacy in migraine result in frequent changes in the treatment strategy.

Translational relevance of baseline peripheral blood biomarkers to assess the efficacy of anti-programmed cell death 1 use in solid malignancies.

This study is an overall clinical analysis of anti-programmed cell death 1 (PD1) antibodies used in a single institution, emphasizing the role of baseline peripheral blood markers as a prognostic or predictor biomarker of immunotherapy. Sixty-one patients were retrospectively analyzed from hospital medical records. The endpoint of this study was death from any cause and the survival time was calculated from the date of start of immunotherapy to the date of death. Descriptive and survival statistics was performed using SPSS version 23. Cutoff values for baseline biomarkers (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], neutrophil-to-eosinophil ratio [NER], and lymphocyte-to-monocyte ratio [LMR]) were obtained using cutp function of Evaluate Cutpoints software (R survMisc package). Pearson and Pearman correlation coefficients were used to examine the relationship of peripheral blood biomarkers. Nighty-eight percent of the study population had Stage IV disease and total median overall survival postanti-PD1 therapy was 10.7 months. Patients receiving more than 5 doses of anti-PD1 therapy (12.6 m vs. 4.4 m, P < 0.001) and used in front lines (18.9 m vs. 10.7 m vs. 10.1 m vs. 2.8 m in first line, second line, third line, and >3 lines, respectively, P = 0.049) were found to have an impact in overall survival. Pembrolizumab showed a better survival compared to nivolumab (17.4 m vs. 8.2 m, P = 0.049) in our study. Among baseline biomarkers assessed, NLR (cutoff - 2.81, P = 0.003) and LMR (cutoff - 5.76, P = 0.017) has shown a statistically significant relationship with immunotherapy response. NER (cutoff - 24.32, P = 0.051) and PLR (cutoff - 190.8, P = 0.072) were also found to exhibit a strong relationship with anti-PD1 therapy response. NLR exhibits a statistically significant positive correlation with PLR (r = 0.917 P < 0.001) and NER (r = 0.400 P = 0.014). Real-life data analysis of anti-PD1 use for solid cancers highlights that baseline NLR, PLR, NER, and LMR have a significant role as immunotherapy biomarkers. However, larger studies are required to further prove the specificity and sensitivity.

Artifact Correction in Short-Term HRV during Strenuous Physical Exercise.

Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results.

A General Framework to Compare Announcement Accuracy: Static vs. LES-Based Announcement

Service providers often share delay information, in the form of delay announcements, with their customers. In practice, simple delay announcements, such as average waiting times or a weighted average of previously delayed customers, are often used. Our goal in this paper is to gain insight into when such announcements perform well. Specifically, we compare the accuracies of two announcements: (i) a static announcement that does not exploit real-time information about the state of the system and (ii) a dynamic announcement, specifically the last-to-enter-service (LES) announcement, which equals the delay of the last customer to have entered service at the time of the announcement. We propose a novel correlation-based approach that is theoretically appealing because it allows for a comparison of the accuracies of announcements across different queueing models, including multiclass models with a priority service discipline. It is also practically useful because estimating correlations is much easier than fitting an entire queueing model. Using a combination of queueing-theoretic analysis, real-life data analysis, and simulation, we analyze the performance of static and dynamic announcements and derive an appropriate weighted average of the two which we demonstrate has a superior performance using both simulation and data from a call center. This paper was accepted by Vishal Gaur, operations management.

Inference about time-dependent prognostic accuracy measures in the presence of competing risks

BackgroundEvaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of competing risks. In this manuscript, we propose a local and a global estimators of cause-specific AUC for right-censored survival times in the presence of competing risks.MethodsThe local estimator - cause-specific weighted mean rank (cWMR) - is a local average of time-specific observed cause-specific AUCs within a neighborhood of given time t. The global estimator - cause-specific fractional polynomials (cFPL) - is based on modelling the cause-specific AUC as a function of t through fractional polynomials.ResultsWe investigated the performance of the proposed cWMR and cFPL estimators through simulation studies and real-life data analysis. The estimators perform well in small samples, have minimal bias and appropriate coverage.ConclusionsThe local estimator cWMR and the global estimator cFPL will provide computationally efficient options for assessing the prognostic accuracy of markers for time-to-event outcome in the presence of competing risks in many practical settings.

An evaluation of design-based properties of different composite estimators

Abstract For the last several decades, the US Census Bureau has been applying AK composite estimation method for estimating monthly levels and month-to-month changes in unemployment using data from the Current Population Survey (CPS), which uses a rotating panel design. For each rotation group, survey-weighted totals, known as month-in-sample estimates, are derived each month to estimate population totals. Denoting the vector of month-in-sample estimates by Y and the design-based variance-covariance matrix of Y by ∑, one can obtain a class of AK estimators as linear combinations of Y, where the coefficients of a linear combination in the class are functions of the two coefficients A and K. The coefficients A and K were optimized by the Census Bureau under rather strong assumptions on ∑ such as the stationarity of ∑ over a decade. We devise an evaluation study in order to compare the AK estimator with a number of rival estimators. To this end, we construct three different synthetic populations that resemble the Current Population Survey (CPS) data. To draw samples from these synthetic populations, we consider a simplified sample design that mimics the CPS sample design with the same rotation pattern. Since the number of possible samples that can be drawn from each synthetic population is not large, we compute the exact ∑ and the exact mean squared error of all estimators considered to facilitate comparison. To generate the first set of rival estimators, we consider certain subclasses of the broader class of linear combinations of month-in-sample estimates. For each subclass, when ∑ is known, the optimum estimator is obtained as a function of ∑. An estimated optimal estimator in each subclass is then obtained from the corresponding optimal estimator when ∑ is replaced by an estimator. Neither the AK estimator nor the estimated optimal estimators for these subclasses performed well in our evaluation study. In our real life data analysis, the AK estimates are constantly below the survey-weighted estimates, indicating potential bias. Our study indicates limitations of the approach that generate an estimated optimal estimator by first obtaining the optimal estimator in a class of linear combination of Y and then substituting in the optimal estimator an estimate of ∑. Any attempt to improve on the estimated optimal estimator in any given class would require a thorough investigation of the highly non-trivial problem of estimation of ∑ for a complex setting like the CPS. We have not discussed this problem in this paper. Instead, we adapted the regression composite estimator used by Statistics Canada in the CPS setting. Unlike the estimated optimal estimators, the regression composite estimator does not require estimation of ∑ and is less sensitive to the rotation group bias in our simulation study. Our study indicates that there is a great potential for regression composite estimation technique in improving estimation of both levels and month-to-month changes in the unemployment rates.

IMPACT OF SMOKING AND DECREASED LUNG FUNCTION ON THE PROGNOSIS OF FEMALE LUNG CANCER: ANALYSIS OF THE KOREAN ASSOCIATION OF LUNG CANCER REGISTRY

TYPE: Abstract Publication TOPIC: Lung Cancer PURPOSE: The clinical characteristics of smokers were reported to be less favorable than those in the never-smoker population of women with lung cancer. However, the accumulation and analysis of more real-life data are necessary. The present study evaluated Korean women with lung cancer and compared the clinical characteristics of ever-smoker and never-smoker groups using the National Lung Cancer Registry. METHODS: In affiliation with the Korean Central Cancer Registry (KCCR), the Korean Association for Lung Cancer (KALC) constructed a registry (KALC-R) into which 10% of the lung cancer cases from all Korean nationals were registered after the survey. Female lung cancer patients with valid smoking history were evaluated. RESULTS: A total of 735 female lung cancer patients were selected and evaluated. There were 643 (87.5%) never-smokers and 92 (12.5%) smokers. The median survival was significantly longer in the never-smoker group. Among 683 patients with non-small cell lung cancer (NSCLC), the never-smoker group showed significantly longer median survival and a higher proportion of stage I cancer. Among 481 patients who underwent epidermal growth factor receptor (EGFR) tests, 248 (51.6%) were EGFR wild-type and 233 (48.4%) were EGFR mutation-positive. Survival analysis of the NSCLC patients showed that smoking status, receiving only supportive care for treatment, EGFR mutation status, lung cancer stage, and forced vital capacity (FVC) (%) were significantly associated with mortality in the multivariate analysis. CONCLUSIONS: In Korean women with NSCLC, smoking experience and poor lung function were independent predictors of shorter overall survival. CLINICAL IMPLICATIONS: Never-smoker female lung cancer has different clinical implication compared with ever-smoker lung cancer. DISCLOSURE: No significant relationships. KEYWORDS: lung cancer, female, Korea