VP07 Collaboratively Modelling The Impact Of Interventions Retrospectively

2017 ◽  
Vol 33 (S1) ◽  
pp. 149-149
Author(s):  
Gordon Bache ◽  
Sukh Tatla ◽  
Deborah Simpson
Keyword(s):  
Real World ◽  
Real Data ◽  
System Level ◽  
Local Data ◽  
Real World Data ◽  
Total Saving ◽  
Retrospective Assessment ◽  
Administration Time ◽  
The Individual ◽  
The Impact

INTRODUCTION:A conventional approach to communicating value is to model the budget impact of a medicine and the associated formulations in which it is available to be prescribed. However, such an approach does not demonstrate the actual realization of the proposed impact. This abstract outlines an approach to presenting retrospective data back to healthcare professionals (HCP) that blends assumptions and real-world data. For illustrative purposes, we present the results of an application of the model for subcutaneously delivered trastuzumab in an anonymized trust in Yorkshire and Humber.METHODS:The authors developed a model that examined one calendar year (from April 2014) of redistributed sales data for both the intravenous and subcutaneous formulations of trastuzumab for every National Health Service (NHS) trust in England. A series of baseline assumptions (1) were used to model the resource impact of different formulations such as chair time, HCP time, pharmacy preparation time, consumables, wastage, and other considerations. Impacts were estimated at the individual attendance level and scaled to the caseload. These baseline assumptions could then be overwritten by the individual trust using local data.RESULTS:The site delivered approximately 985 doses of subcutaneous trastuzumab over a period of 12 months from April 2014, which represented about 76 percent of the total number of doses delivered. Chair time is estimated to have reduced by 22 minutes per attendance, resulting in a total saving of 361hours. HCP administration time is estimated to have reduced by 23 minutes per attendance, resulting in a total saving of 378 hours based on changing 985 IV doses to SC therapy.CONCLUSIONS:Blending real data and assumptions to provide a retrospective assessment of actual benefits realized back to HCPs is a powerful tool for demonstrating real-world value at both an individual trust and system level.

scholarly journals The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

2021 ◽  
Author(s):  
Dariusz Brzezinski ◽  
Leandro L. Minku ◽  
Tomasz Pewinski ◽  
Jerzy Stefanowski ◽  
Artur Szumaczuk
Keyword(s):  
Data Streams ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Local Data ◽  
Real World Data ◽  
Minority Class ◽  
The Impact ◽  
Concept Drifts

AbstractClass imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers’ reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

scholarly journals NCDB Analysis of Melanoma 2004–2015: Epidemiology and Outcomes by Subtype, Sociodemographic Factors Impacting Clinical Presentation, and Real-World Survival Benefit of Immunotherapy Approval

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1455
Author(s):  
Sunny R. K. Singh ◽  
Sindhu J. Malapati ◽  
Rohit Kumar ◽  
Christopher Willner ◽  
Ding Wang
Keyword(s):  
Metastatic Melanoma ◽  
Metastatic Disease ◽  
Real World ◽  
Care Delivery ◽  
Sociodemographic Factors ◽  
P Value ◽  
Real World Data ◽  
Factors Associated ◽  
Improvement In Survival ◽  
The Impact

Background: The incidence of invasive melanoma is rising, and approval for the first immune checkpoint inhibitor (ICI) to treat metastatic melanoma occurred in 2011. We aim to describe the epidemiology and outcomes in recent years, sociodemographic factors associated with the presence of metastasis at diagnosis, and the real‐world impact of ICI approval on survival based on melanoma subtype and race. Methods: This is a retrospective analysis of the National Cancer Database (NCDB) from the years 2004–2015. The primary outcome was the overall survival of metastatic melanoma by subtype. Secondary outcomes included sociodemographic factors associated with the presence of metastasis at diagnosis and the impact of treatment facility type and ICI approval on the survival of metastatic melanoma. Results: Of the 419,773 invasive melanoma cases, 93.80% were cutaneous, and 4.92% were metastatic at presentation. The odds of presenting with metastatic disease were higher in African Americans (AA) compared to Caucasians (OR 2.37; 95% CI 2.11–2.66, p < 0.001). Treatment of metastatic melanoma at an academic/research facility was associated with lower mortality versus community cancer programs (OR 0.75, 95 % CI 0.69–0.81, p-value<0.001). Improvement in survival of metastatic melanoma was noted for Caucasians after the introduction of ICI (adjusted HR 0.80, 95% CI 0.78–0.83, p < 0.001); however, this was not statistically significant for AA (adjusted HR 0.80, 95% CI 0.62–1.02, p‐value = 0.073) or ocular cases (HR 1.03, 95% CI 0.81–1.31, p‐value 0.797). Conclusion: Real‐world data suggest a 20% improvement in survival of metastatic melanoma since the introduction of ICI. The disproportionately high odds of metastatic disease at presentation in AA patients with melanoma suggest the need for a better understanding of the disease and improvement in care delivery.

PD-0776 A Bayesian approach to evaluate the impact of change in IGRT protocol using real world data

2021 ◽  
Vol 161 ◽  
pp. S608
Author(s):  
I. Fornacon-Wood ◽  
H. Mistry ◽  
C. Johnson-Hart ◽  
J.P.B. O’Connor ◽  
C. Faivre-Finn ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
The Impact

Assessment and design of real world driving cycles targeted to the calibration of vehicles with electrified powertrain

2021 ◽  
pp. 146808742110387
Author(s):  
Stylianos Doulgeris ◽  
Zisimos Toumasatos ◽  
Maria Vittoria Prati ◽  
Carlo Beatrice ◽  
Zissis Samaras
Keyword(s):  
Real World ◽  
Simulation Models ◽  
Cost Effective ◽  
Operational Parameters ◽  
Virtual Design ◽  
Passenger Cars ◽  
Real World Data ◽  
Vehicle Simulation ◽  
Driving Cycles ◽  
The Impact

Vehicles’ powertrain electrification is one of the key measures adopted by manufacturers in order to develop low emissions vehicles and reduce the CO2 emissions from passenger cars. High complexity of electrified powertrains increases the demand of cost-effective tools that can be used during the design of such powertrain architectures. Objective of the study is the proposal of a series of real-world velocity profiles that can be used during virtual design. To that aim, using three state of the art plug-in hybrid vehicles, a combined experimental, and simulation approach is followed to derive generic real-world cycles that can be used for the evaluation of the overall energy efficiency of electrified powertrains. The vehicles were tested under standard real driving emissions routes, real-world routes with reversed order (compared to a standard real driving emissions route) of urban, rural, motorway, and routes with high slope variation. To enhance the experimental activities, additional virtual mission profiles simulated using vehicle simulation models. Outcome of the study consists of specific driving cycles, designed based on standard real-world route, and a methodology for real-world data analysis and evaluation, along with the results from the assessment of the impact of different operational parameters on the total electrified powertrain.

The failure of certain fractional calculus operators in two physical models

2019 ◽  
Vol 22 (2) ◽  
pp. 255-270 ◽  
Author(s):  
Manuel D. Ortigueira ◽  
Valeriy Martynyuk ◽  
Mykola Fedula ◽  
J. Tenreiro Machado
Keyword(s):  
Fractional Calculus ◽  
Human Body ◽  
Real World ◽  
Fractional Derivatives ◽  
Electrical Impedance ◽  
Real Data ◽  
Physical Models ◽  
Data Sets ◽  
Real World Data ◽  
Fractional Calculus Operators

Abstract The ability of the so-called Caputo-Fabrizio (CF) and Atangana-Baleanu (AB) operators to create suitable models for real data is tested with real world data. Two alternative models based on the CF and AB operators are assessed and compared with known models for data sets obtained from electrochemical capacitors and the human body electrical impedance. The results show that the CF and AB descriptions perform poorly when compared with the classical fractional derivatives.

VP08 Real-World Data Use In Health Technology Assessments: A Comparison Of Five Health Technology Assessment Agencies

2017 ◽  
Vol 33 (S1) ◽  
pp. 149-150
Author(s):  
Amr Makady ◽  
Ard van Veelen ◽  
Anthonius de Boer ◽  
Hans Hillege ◽  
Olaf Klunger ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Relative Effectiveness ◽  
Internal Validity ◽  
Health Technology ◽  
Real World Data ◽  
World Data ◽  
The Impact

INTRODUCTION:Reimbursement decisions are usually based on evidence from randomized controlled trials (RCT) with high internal validity but lower external validity. Real-World Data (RWD) may provide complimentary evidence for relative effectiveness assessments (REA's) and cost-effectiveness assessments (CEA's) of treatments. This study explores to which extent RWD is incorporated in REA's and CEA's of drugs used to treat metastatic melanoma (MM) by five Health Technology Assessment (HTA) agencies.METHODS:Dossiers for MM drugs published between 1 January 2011 and 31 December 2016 were retrieved for HTA agencies in five countries: the United Kingdom (NICE), Scotland (SMC), France (HAS), Germany (IQWiG) and the Netherlands (ZIN). A standardized data-extraction form was used to extract data on RWD mentioned in the assessment and its impact on appraisal (for example, positive, negative, neutral or unknown) for both REA and CEA.RESULTS:In total, fourty-nine dossiers were retrieved: NICE = 10, SMC = 13, IQWiG = 16, HAS = 8 and ZIN = 2. Nine dossiers (18.4 percent) included RWD in REA's for several parameters: to describe effectiveness (n = 5) and/or the safety (n = 2) of the drug, and/or the prevalence of MM (n = 4). CEA's were included in 25/49 dossiers (IQWiG and HAS did not perform CEA's). Of the twenty-five CEA's, twenty (80 percent) included RWD to extrapolate long-term effectiveness (n = 19), and/or identify costs associated with treatments (n = 7). When RWD was included in REA's (n = 9), its impact on the appraisal was negative (n = 4), neutral (n = 2), unknown (n = 1) or was not discussed in the appraisal (n = 2). When RWD was included in CEA's (n = 11), its impact on the appraisal varied between positive (n = 2), negative (n = 5) and unknown (n = 4).CONCLUSIONS:Generally, RWD is more often included in CEA's than REA's (80 percent versus 18.4 percent, respectively). When included, RWD was mostly used to describe the effectiveness of the drug (REA) or to predict long-term effectiveness (CEA). The impact of RWD on the appraisal varied greatly within both REA's and CEA's.

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