test validation
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2022 ◽  
Vol 27 (1) ◽  
pp. 100543
Author(s):  
Marlone Cunha-Silva ◽  
Fernando L. Ponte Neto ◽  
Priscila S. de Araújo ◽  
Lucas V. Pazinato ◽  
Raquel D. Greca ◽  
...  

2021 ◽  
Author(s):  
Mohamad Mustaqim Mokhlis ◽  
Nurdini Alya Hazali ◽  
Muhammad Firdaus Hassan ◽  
Mohd Hafiz Hashim ◽  
Afzan Nizam Jamaludin ◽  
...  

Abstract In this paper we will present a process streamlined for well-test validation that involves data integration between different database systems, incorporated with well models, and how the process can leverage real-time data to present a full scope of well-test analysis to enhance the capability for assessing well-test performance. The workflow process demonstrates an intuitive and effective way for analyzing and validating a production well test via an interactive digital visualization. This approach has elevated the quality and integrity of the well-test data, as well as improved the process cycle efficiency that complements the field surveillance engineers to keep track of well-test compliance guidelines through efficient well-test tracking in the digital interface. The workflow process involves five primary steps, which all are conducted via a digital platform: Well Test Compliance: Planning and executing the well test Data management and integration Well Test Analysis and Validation: Verification of the well test through historical trending, stability period checks, and well model analysis Model validation: Correcting the well test and calibrating the well model before finalizing the validity of the well test Well Test Re-testing: Submitting the rejected well test for retesting and final step Integrating with corporate database system for production allocation This business process brings improvement to the quality of the well test, which subsequently lifts the petroleum engineers’ confidence level to analyze well performance and deliver accurate well-production forecasting. A well-test validation workflow in a digital ecosystem helps to streamline the flow of data and system integration, as well as the way engineers assess and validate well-test data, which results in minimizing errors and increases overall work efficiency.


BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e050146
Author(s):  
Jenna M Reps ◽  
Patrick Ryan ◽  
P R Rijnbeek

ObjectiveThe internal validation of prediction models aims to quantify the generalisability of a model. We aim to determine the impact, if any, that the choice of development and internal validation design has on the internal performance bias and model generalisability in big data (n~500 000).DesignRetrospective cohort.SettingPrimary and secondary care; three US claims databases.Participants1 200 769 patients pharmaceutically treated for their first occurrence of depression.MethodsWe investigated the impact of the development/validation design across 21 real-world prediction questions. Model discrimination and calibration were assessed. We trained LASSO logistic regression models using US claims data and internally validated the models using eight different designs: ‘no test/validation set’, ‘test/validation set’ and cross validation with 3-fold, 5-fold or 10-fold with and without a test set. We then externally validated each model in two new US claims databases. We estimated the internal validation bias per design by empirically comparing the differences between the estimated internal performance and external performance.ResultsThe differences between the models’ internal estimated performances and external performances were largest for the ‘no test/validation set’ design. This indicates even with large data the ‘no test/validation set’ design causes models to overfit. The seven alternative designs included some validation process to select the hyperparameters and a fair testing process to estimate internal performance. These designs had similar internal performance estimates and performed similarly when externally validated in the two external databases.ConclusionsEven with big data, it is important to use some validation process to select the optimal hyperparameters and fairly assess internal validation using a test set or cross-validation.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4928-4928
Author(s):  
Maryam Sarraf Yazdy ◽  
Andrea C. Baines ◽  
Theresa Carioti ◽  
Rachel Ershler ◽  
Emily Y. Jen ◽  
...  

Abstract Introduction: In the past decade, multiple studies have reported the prognostic and predictive value of MRD status in specific hematologic malignancies (HM). Because clinical trials are increasingly incorporating MRD status as a biomarker and efficacy endpoint, the adequacy of the MRD data to inform the prescribing information (PI) is relevant for the design and conduct of pivotal clinical trials. We present an analysis of the trends in inclusion of MRD data in pivotal trials in HMs and regulatory decisions made by the U.S. Food and Drug Administration (FDA). Methods: We reviewed FDA internal databases for original and supplemental new drug applications (NDAs) and biologics licensing applications (BLAs) submitted 1/2014-12/2020 to support approval of therapies (drugs, biologics, and cellular therapies) for HM. MRD data were evaluated for two time periods to inform potential trends: 1/2014-6/2017 (period 1) and 7/2017-12/2020 (period 2). Clinical study reports, selected datasets, FDA clinical reviews, and the proposed and approved PIs were examined for inclusion of MRD data, and FDA assessments of the adequacy of the MRD data for inclusion in the PI were reviewed. Results: Of 196 NDAs or BLAs involving HM submitted between 2014-2021, 53 (27%) had MRD data, including 53 pivotal trials. The trials included patients with chronic lymphocytic leukemia, chronic myeloid leukemia, acute myeloid leukemia, acute lymphocytic leukemia, and multiple myeloma. Twenty-one applications and pivotal trials with MRD data were submitted in period 1, and 32 applications and 35 trials were submitted in period 2. Three trials were resubmitted in period 2. MRD evaluation was specified as a secondary and exploratory endpoint in 35 (66%) and 19 (36%) of the trials, respectively. Of the 53 trials, MRD data was proposed by the Applicant for inclusion in the PI in 41 (77%) but was ultimately included in 25 (47%). Of the trials for which MRD data was proposed in labeling, MRD data were deemed adequate by FDA in 81% of studies in period 1 (13/16) and 48% of studies in period 2 (12/25). MRD assays in the PI included polymerase chain reaction, flow cytometry, and next-generation sequencing in 18 (72%), 5 (20%) and 4 (16%) of the trials, respectively, with the clinical threshold for test positivity ranging from 10 -3 to 10 -5. For 11 trials with MRD data in the PI (44%), the MRD was evaluated regardless of clinical response, and in 14 trials (56%) MRD was evaluated in patients achieving a specific clinical response. The leading reasons for excluding MRD data from the PI were analytical and test validation deficiencies (e.g., incomplete test characteristics data, lack of test validation overall or in that disease) followed by performance issues (e.g., high amount of test failure, inability to identify a clone) and issues with trial conduct or design (e.g., inadequate data collection, statistical issues). Conclusion: A quarter of HM drug applications, including 53 pivotal trials, submitted to the FDA between 2014-2020 included MRD data. Characterization of regulatory actions showed that despite the increasing number of submissions proposing MRD data for inclusion in the PI, rates of inclusion of MRD data in the PI did not reflect this increase. Improvements in assay validation and performance characteristics, robust collection of MRD data, and appropriate statistical planning can enable greater representation of MRD data in prescription drug labeling. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 2093 (1) ◽  
pp. 012010
Author(s):  
Xiang Li ◽  
Lijun Tong ◽  
Libing Cai ◽  
Qian Xie ◽  
Jinling Xu ◽  
...  

Abstract RF (Radio Frequency) connector has been extensively applied in the field of communication for it is a key device of transmitting microwave signals. If he RF connector end is designed improperly in dimension, a clearance will be observed after inserting, resulting in an abnormal phenomenon in the voltage standing wave ratio (VSWR) of the RF connector. This paper studied the influence of the inserting clearance of RF connector on the voltage standing wave ratio through theoretical analysis and performance simulation. Moreover, the solution to the abnormal voltage standing wave ratio was also proposed. At last, the abnormal phenomenon of voltage standing wave ratio of the connector in the inserting process was handled by means of carrying out optimization design and test validation on the RF connector.


2021 ◽  
Author(s):  
Matej Kucera ◽  
Radek Reznicek ◽  
Michal Pfleger ◽  
Milos Sotak ◽  
Zdenek Kana ◽  
...  
Keyword(s):  

Author(s):  
Jue Shi ◽  
Run-Qing Mu ◽  
Pan Wang ◽  
Wen-Qing Geng ◽  
Yong-Jun Jiang ◽  
...  

Abstract Objectives Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform. Methods A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation. Results Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system. Conclusions The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions.


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