scholarly journals The Shape of ROC Curves in Shooter Tasks: Implications for Best Practices in Analysis

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Caren M. Rotello ◽  
Laura J. Kelly ◽  
Evan Heit
Keyword(s):  
Best Practices ◽  
Signal Detection ◽  
Roc Curves ◽  
Receiver Operating Characteristics ◽  
Racial Groups ◽  
Operating Characteristics ◽  
Identification Task ◽  
Data Analyses ◽  
Linear Receiver ◽  
First Person Shooter

Four experiments addressed the widely studied issue of the association between racial groups and guns, namely shooter bias, as measured in the first-person shooter task or the weapon identification task, in which participants judge whether a suspect has a weapon or some other item such as a phone (Correll, Park, Judd, & Wittenbrink, 2002; Payne, 2001). Previous studies have employed various analyses that make conflicting, and indeed untested, assumptions about the underlying nature of the data: Analyses of variance and model-based analyses assume linear receiver operating characteristics (ROCs) and signal detection (SDT) analyses assume curved ROCs. The present experiments directly investigated the shape of the ROCs for the weapon identification task, demonstrating that they are curved, and that the majority of previous studies are at risk for inclusion of inappropriate analyses, because they assume linear rather than curved ROCs.

scholarly journals Detecting paraprotein interference on a direct bilirubin assay by reviewing the photometric reaction data

2017 ◽  
Vol 55 (8) ◽  
pp. 1178-1185 ◽  
Author(s):  
Elena García-González ◽  
Maite Aramendía ◽  
Ricardo González-Tarancón ◽  
Naiara Romero-Sánchez ◽  
Luis Rello
Keyword(s):  
Roc Curves ◽  
Direct Bilirubin ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Monoclonal Gammopathies ◽  
Reaction Data ◽  
Receiver Operating

Abstract Background: The direct bilirubin (D-Bil) assay on the AU Beckman Coulter instrumentation can be interfered by paraproteins, which may result in spurious D-Bil results. In a previous work, we took advantage of this fact to detect this interference, thus helping with the identification of patients with unsuspected monoclonal gammopathies. In this work, we investigate the possibility to detect interference based on the review of the photometric reactions, regardless of the D-Bil result. Methods: The D-Bil assay was carried out in a set of 2164 samples. It included a group of 164 samples with paraproteins (67 of which caused interference on the assay), as well as different groups of samples for which high absorbance background readings could also be expected (i.e. hemolyzed, lipemic, or icteric samples). Photometric reaction data were reviewed and receiver operating characteristics (ROC) curves were used to establish a cut-off for absorbance that best discriminates interference. Results: The best cut-off was 0.0100 for the absorbance at the first photometric point of the complementary wavelength in the blank cuvette. Once the optimal cut-off for probable interference was selected, all samples analyzed in our laboratory that provided absorbance values above this cut-off were further investigated to try to discover paraproteins. During a period of 6 months, we detected 44 samples containing paraproteins, five of which belonged to patients with non-diagnosed monoclonal gammopathies. Conclusions: Review of the photometric reaction data permits the systematic detection of paraprotein interference on the D-Bil AU assay, even for samples for which reasonable results are obtained.

Detectability of simulated masses and calcifications in mammography

1998 ◽  
Vol 39 (5) ◽  
pp. 501-506 ◽  
Author(s):  
J. B. Olsen ◽  
A. Skretting
Keyword(s):  
Mathematical Model ◽  
Least Squares ◽  
Breast Tissue ◽  
Detection Threshold ◽  
Roc Curves ◽  
Receiver Operating Characteristics ◽  
Linear Least Squares ◽  
Operating Characteristics ◽  
Least Squares Fit ◽  
Novel Method

Purpose: to develop a method for receiver operating characteristics (ROC) studies in mammography Material and Methods: We developed a phantom based on excised breast tissue and overlay tiles that could be arranged in an arbitrary pattern across the surface of the breast tissue. Some of the tiles contained structures simulating calcifications or masses that produced image contrast near the experimentally determined detection threshold. Based on this phantom, a methodology for performing ROC studies in mammography was developed. the ROC curves were constructed from reporting schemes filled in by radiologists at five different laboratories. the curves were determined by a novel method: a non-linear least-squares fit of a mathematical model to the data Results: There were large differences among the areas under the ROC curves obtained from the five laboratories

Assessment of image quality and total performance in norwegian mammography laboratories

1998 ◽  
Vol 39 (5) ◽  
pp. 507-513
Author(s):  
J. B. Olsen ◽  
A. Skretting ◽  
A. Widmark
Keyword(s):  
Image Quality ◽  
Breast Tissue ◽  
Roc Analysis ◽  
Roc Curves ◽  
Receiver Operating Characteristics ◽  
Mammary Tissue ◽  
Physical Parameters ◽  
Operating Characteristics ◽  
Tissue Phantom ◽  
Total Performance

Purpose: to assess the image quality at different mammography laboratories Material and Methods: Two commercial mammographic test phantoms and one phantom based on excised mammary tissue were used in an assessment of the imaging chain and total performance at 45 Norwegian mammography laboratories. the breast-tissue phantom was used for a receiver operating characteristics (ROC) analysis. This was carried out by putting overlays with identifiable regions (some of which contained a cluster of simulated calcifications) on top of the mammary tissue, and then having a radiologist report the confidence of a finding for each region Results and Conclusion: the areas under the ROC curves were in general high. in nearly all the laboratories, performance was improved when a magnification technique was applied. There were wide variations among the laboratories in total performance as measured by the area under the ROC curve, and also in the physical parameters derived by means of the commercial phantoms. in general, a good ROC performance was associated with a good physical performance in the imaging chain

scholarly journals Comparison of a Bayesian Network with a Logistic Regression Model to Forecast IgA Nephropathy

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Michel Ducher ◽  
Emilie Kalbacher ◽  
François Combarnous ◽  
Jérome Finaz de Vilaine ◽  
Brigitte McGregor ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Practice ◽  
Bayesian Network ◽  
Iga Nephropathy ◽  
Logistic Regression Model ◽  
Roc Curves ◽  
Biological Data ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Biological Criteria

Models are increasingly used in clinical practice to improve the accuracy of diagnosis. The aim of our work was to compare a Bayesian network to logistic regression to forecast IgA nephropathy (IgAN) from simple clinical and biological criteria. Retrospectively, we pooled the results of all biopsies(n=155)performed by nephrologists in a specialist clinical facility between 2002 and 2009. Two groups were constituted at random. The first subgroup was used to determine the parameters of the models adjusted to data by logistic regression or Bayesian network, and the second was used to compare the performances of the models using receiver operating characteristics (ROC) curves. IgAN was found (on pathology) in 44 patients. Areas under the ROC curves provided by both methods were highly significant but not different from each other. Based on the highest Youden indices, sensitivity reached (100% versus 67%) and specificity (73% versus 95%) using the Bayesian network and logistic regression, respectively. A Bayesian network is at least as efficient as logistic regression to estimate the probability of a patient suffering IgAN, using simple clinical and biological data obtained during consultation.

Kriteriumsbezogene Cut-Off-Werte für Tätigkeitsspielraum und Arbeitsintensität

Diagnostica ◽  
2019 ◽  
Vol 65 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Vincent Mustapha ◽  
Renate Rau
Keyword(s):  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Receiver Operating

Zusammenfassung. Cut-Off-Werte ermöglichen eine ökonomische, binäre Beurteilung von Summenscores. Für Beanspruchungsfragebögen, die personenbezogene Merkmale erfragen, sind Cut-Off-Werte häufig vorhanden und in der klinischen Diagnostik unerlässlich. Für die Bewertung von Arbeitsmerkmalen sind Cut-Off-Werte ebenfalls wünschenswert. Bislang fehlen sie jedoch für die Beurteilung von Arbeitsmerkmalen wie Arbeitsintensität und Tätigkeitsspielraum. Zwischen 2006 und 2016 wurden daher in verschiedenen Branchen 801 objektive Arbeitsplatzanalysen durchgeführt, welche eine Unterteilung in gut und schlecht gestalteten Tätigkeitsspielraum sowie gut und schlecht gestaltete Arbeitsintensität nach DIN EN ISO 6385 (2016) ermöglichen. Anhand dieser Unterteilung wurden mit der Receiver-Operating-Characteristics-Analyse Cut-Off-Werte für den subjektiv-bedingungsbezogen Fragebogen zum Erleben von Arbeitsintensität und Tätigkeitsspielraum (FIT; Richter et al., 2000 ) ermittelt. Für den Tätigkeitsspielraum weisen Summenscores ≤ 22 und für die Arbeitsintensität Summenscores ≥ 15 auf eine schlechte Gestaltung des jeweiligen Arbeitsmerkmals hin. Anhand einer weiteren Stichprobe von 1 076 Arbeitenden konnte gezeigt werden, dass Arbeitende mit schlecht gestaltetem Tätigkeitspielraum vital erschöpfter sowie weniger engagiert sind und Arbeitende mit schlecht gestalteter Arbeitsintensität eine höhere Erholungsunfähigkeit sowie vitale Erschöpfung aufweisen.

Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians

1991 ◽  
Vol 30 (03) ◽  
pp. 187-193 ◽  
Author(s):  
H. J. Moens ◽  
J. K. van der Korst
Keyword(s):  
Receiver Operating Characteristics ◽  
Bayesian Decision ◽  
Operating Characteristics ◽  
Likelihood Ratios ◽  
Improve Performance ◽  
Mathematical Basis ◽  
Human Expert ◽  
Test Population ◽  
Sensitivity Specificity ◽  
Diagnostic Outcome

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.

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