Evaluation of partial area under the curve in bioequivalence studies using destructive sampling design

2020 ◽  
Author(s):  
Marilyn N. Martinez ◽  
Shasha Gao
Keyword(s):  
Sampling Design ◽  
Area Under The Curve ◽  
Destructive Sampling ◽  
Partial Area

scholarly journals Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

Metabolites ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 90 ◽  
Author(s):  
Elizabeth C. Considine ◽  
Ali S. Khashan ◽  
Louise C. Kenny
Keyword(s):  
Preterm Birth ◽  
Area Under The Curve ◽  
Maternal Serum ◽  
P Value ◽  
Spontaneous Preterm Birth ◽  
Serum Samples ◽  
Biomarker Panel ◽  
Partial Area ◽  
High Concordance ◽  
Univariate Analyses

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

scholarly journals Validation of radiologists’ findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software

2019 ◽  
Vol 61 (3) ◽  
pp. 312-320 ◽  
Author(s):  
Jan CM van Zelst ◽  
Tao Tan ◽  
Ritse M Mann ◽  
Nico Karssemeijer
Keyword(s):  
Breast Cancer ◽  
Area Under The Curve ◽  
Breast Ultrasound ◽  
Computer Aided Detection ◽  
Benign Lesions ◽  
Partial Area ◽  
Computer Aided ◽  
Before And After ◽  
Detection Software ◽  
Automated Breast Ultrasound

Background Computer-aided detection software for automated breast ultrasound has been shown to have potential in improving the accuracy of radiologists. Alternative ways of implementing computer-aided detection, such as independent validation or preselecting suspicious cases, might also improve radiologists’ accuracy. Purpose To investigate the effect of using computer-aided detection software to improve the performance of radiologists by validating findings reported by radiologists during screening with automated breast ultrasound. Material and Methods Unilateral automated breast ultrasound exams were performed in 120 women with dense breasts that included 60 randomly selected normal exams, 30 exams with benign lesions, and 30 malignant cases (20 mammography-negative). Eight radiologists were instructed to detect breast cancer and rate lesions using BI-RADS and level-of-suspiciousness scores. Computer-aided detection software was used to check the validity of radiologists' findings. Findings found negative by computer-aided detection were not included in the readers’ performance analysis; however, the nature of these findings were further analyzed. The area under the curve and the partial area under the curve for an interval in the range of 80%–100% specificity before and after validation of computer-aided detection were compared. Sensitivity was computed for all readers at a simulation of 90% specificity. Results Partial AUC improved significantly from 0.126 (95% confidence interval [CI] = 0.098–0.153) to 0.142 (95% CI = 0.115–0.169) ( P = 0.037) after computer-aided detection rejected mostly benign lesions and normal tissue scored BI-RADS 3 or 4. The full areas under the curve (0.823 vs. 0.833, respectively) were not significantly different ( P = 0.743). Four cancers detected by readers were completely missed by computer-aided detection and four other cancers were detected by both readers and computer-aided detection but falsely rejected due to technical limitations of our implementation of computer-aided detection validation. In this study, validation of computer-aided detection discarded 42.6% of findings that were scored BI-RADS ≥3 by the radiologists, of which 85.5% were non-malignant findings. Conclusion Validation of radiologists’ findings using computer-aided detection software for automated breast ultrasound has the potential to improve the performance of radiologists. Validation of computer-aided detection might be an efficient tool for double-reading strategies by limiting the amount of discordant cases needed to be double-read.

scholarly journals Use of Partial Area under the Curve Metrics to Assess Bioequivalence of Methylphenidate Multiphasic Modified Release Formulations

2012 ◽  
Vol 14 (4) ◽  
pp. 925-926 ◽  
Author(s):  
Ethan M. Stier ◽  
Barbara M. Davit ◽  
Parthapratim Chandaroy ◽  
Mei-Ling Chen ◽  
Jeanne Fourie-Zirkelbach ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Modified Release ◽  
Partial Area

scholarly journals On the Use of Min-Max Combination of Biomarkers to Maximize the Partial Area under the ROC Curve

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Hua Ma ◽  
Susan Halabi ◽  
Aiyi Liu
Keyword(s):  
Roc Curve ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Roc Curves ◽  
Covariance Matrices ◽  
Distributed Data ◽  
Multivariate Distributions ◽  
Unequal Variance ◽  
Line Shapes ◽  
Partial Area

Background. Evaluation of diagnostic assays and predictive performance of biomarkers based on the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are vital in diagnostic and targeted medicine. The partial area under the curve (pAUC) is an alternative metric focusing on a range of practical and clinical relevance of the diagnostic assay. In this article, we adopt and extend the min-max method to the estimation of the pAUC when multiple continuous scaled biomarkers are available and compare the performances of our proposed approach with existing approaches via simulations. Methods. We conducted extensive simulation studies to investigate the performance of different methods for the combination of biomarkers based on their abilities to produce the largest pAUC estimates. Data were generated from different multivariate distributions with equal and unequal variance-covariance matrices. Different shapes of the ROC curves, false positive fraction ranges, and sample size configurations were considered. We obtained the mean and standard deviation of the pAUC estimates through re-substitution and leave-one-pair-out cross-validation. Results. Our results demonstrate that the proposed method provides the largest pAUC estimates under the following three important practical scenarios: (1) multivariate normally distributed data for nondiseased and diseased participants have unequal variance-covariance matrices; or (2) the ROC curves generated from individual biomarker are relative close regardless of the latent normality distributional assumption; or (3) the ROC curves generated from individual biomarker have straight-line shapes. Conclusions. The proposed method is robust and investigators are encouraged to use this approach in the estimation of the pAUC for many practical scenarios.

scholarly journals “Only your first yes will count”: The impact of pre-lineup instructions on sequential lineup decisions

2020 ◽  
Author(s):  
Ruth Horry ◽  
Ryan J Fitzgerald ◽  
Jamal K. Mansour
Keyword(s):  
Area Under The Curve ◽  
Diagnostic Value ◽  
Sequential Lineups ◽  
Sequential Lineup ◽  
Criterion Shift ◽  
Sequential Control ◽  
Partial Area ◽  
Simultaneous Lineup ◽  
Mock Crime ◽  
The Impact

When administering sequential lineups, researchers often inform their participants that only their first yes response will count. This instruction differs from the original sequential lineup protocol and from how sequential lineups are conducted in practice. Participants (N = 896) viewed a videotaped mock crime and viewed a simultaneous lineup, a sequential lineup with a first-yes-counts instruction, or a sequential control lineup (with no first-yes-counts instruction); the lineup was either target-present or target-absent. Participants in the first-yes-counts condition were less likely to identify the suspect and more likely to reject the lineup than participants in the simultaneous and sequential control conditions, suggesting a conservative criterion shift. The diagnostic value of suspect identifications, as measured by partial Area Under the Curve, was lower in the first-yes-counts lineup than in the simultaneous lineup. Results were qualitatively similar for other metrics of diagnosticity, though the differences were not statistically significant. Differences between the simultaneous and sequential control lineups were negligible on all outcomes. The first-yes-counts instruction undermines sequential lineup performance and produces an artefactual simultaneous lineup advantage. Researchers should adhere to sequential lineup protocols that maximize diagnosticity and that would feasibly be implemented in practice, allowing them to draw more generalizable conclusions from their data.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

A randomized cross-over trial in patients suspected of PAD on diagnostic accuracy of ankle-brachial index by Doppler-based versus four-point oscillometry based measurements

VASA ◽  
2019 ◽  
Vol 48 (6) ◽  
pp. 516-522 ◽  
Author(s):  
Verena Mayr ◽  
Mirko Hirschl ◽  
Peter Klein-Weigel ◽  
Luka Girardi ◽  
Michael Kundi
Keyword(s):  
Diagnostic Accuracy ◽  
Ankle Brachial Index ◽  
Area Under The Curve ◽  
High Volume ◽  
Epidemiologic Studies ◽  
Lower Sensitivity ◽  
Oscillometric Device ◽  
Specificity And Sensitivity ◽  
Time Requirements ◽  
Peripheral Arterial

Summary. Background: For diagnosis of peripheral arterial occlusive disease (PAD), a Doppler-based ankle-brachial-index (dABI) is recommended as the first non-invasive measurement. Due to limitations of dABI, oscillometry might be used as an alternative. The aim of our study was to investigate whether a semi-automatic, four-point oscillometric device provides comparable diagnostic accuracy. Furthermore, time requirements and patient preferences were evaluated. Patients and methods: 286 patients were recruited for the study; 140 without and 146 with PAD. The Doppler-based (dABI) and oscillometric (oABI and pulse wave index – PWI) measurements were performed on the same day in a randomized cross-over design. Specificity and sensitivity against verified PAD diagnosis were computed and compared by McNemar tests. ROC analyses were performed and areas under the curve were compared by non-parametric methods. Results: oABI had significantly lower sensitivity (65.8%, 95% CI: 59.2%–71.9%) compared to dABI (87.3%, CI: 81.9–91.3%) but significantly higher specificity (79.7%, 74.7–83.9% vs. 67.0%, 61.3–72.2%). PWI had a comparable sensitivity to dABI. The combination of oABI and PWI had the highest sensitivity (88.8%, 85.7–91.4%). ROC analysis revealed that PWI had the largest area under the curve, but no significant differences between oABI and dABI were observed. Time requirement for oABI was significantly shorter by about 5 min and significantly more patients would prefer oABI for future testing. Conclusions: Semi-automatic oABI measurements using the AngER-device provide comparable diagnostic results to the conventional Doppler method while PWI performed best. The time saved by oscillometry could be important, especially in high volume centers and epidemiologic studies.

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