scholarly journals Tort Law and Judicial Risk Regulation: Bipolar and Multipolar Risk Reasoning in Light of Tort Law’s Regulatory Effects

2018 ◽  
Vol 9 (1) ◽  
pp. 14-33
Author(s):  
Elbert R DE JONG
Keyword(s):  
False Positive ◽  
False Negative ◽  
Standard Of Care ◽  
Tort Law ◽  
False Negatives ◽  
Risk Regulation ◽  
Judicial Decisions ◽  
Real Risk ◽  
Reasoning Style ◽  
Regulatory Effects

AbstractAlthough judicial decisions in tort law primarily determine the (correlative) responsibilities and liabilities of the proceeding parties, they also have regulatory effects on non-litigants. In this contribution, these regulatory consequences of tort law will be analysed in light of a court’s quest when it decides a tort claim involving (uncertain) risks. It will be argued that decisions in tort law about uncertain risks involve the possible occurrence of a false positive (eg accepting liability for a non-existing risk) and a false negative (eg denying liability for a real risk). False positives and false negatives have adverse consequences for the parties to the proceedings but, bearing in mind the regulatory effects of tort adjudication, potentially also for non-litigants. While courts might want to avoid both, scientific uncertainties and complexities make it difficult for them to assess to what extent there is a chance of either a false positive or a false negative occurring. Therefore, they necessarily need to determine which party bears the risk of the involved errors. In addition, the question arises whether courts should also take the potential regulatory consequences of their rulings into account and, if yes, how? To that purpose, they can employ a bipolar reasoning style and a multipolar reasoning style. Although tort law is about determining the applicable rights and obligations between the plaintiff and defendant (bipolar reasoning), in light of the regulatory implications of tort law and developments in several tort systems, the relevance of considerations transcending this bipolar relationship (multipolar reasoning) is increasing. However, the possibilities for courts to engage in multipolar reasoning are restrained by the bipolar nature of tort law which gives rise to information and specialism deficits. This will be illustrated by referring to issues in relation to setting the standard of care and examining causation.

Evaluation of Positive T- and B-Cell Gene Rearrangement Studies Among Patients Without a Definitive Diagnosis by Other Assays

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S35-S36
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa Siddon
Keyword(s):  
T Cell ◽  
False Positive ◽  
Gene Rearrangement ◽  
Hematologic Malignancy ◽  
False Negative ◽  
False Positives ◽  
False Negatives ◽  
True Negative ◽  
Flow Cytometric ◽  
Pathology Reports

Abstract In the evaluation of bone marrow (BM) and peripheral blood (PB) for hematologic malignancy, positive immunoglobulin heavy chain (IG) or T-cell receptor (TCR) gene rearrangement results may be detected despite unrevealing results from morphologic, flow cytometric, immunohistochemical (IHC), and/or cytogenetic studies. The significance of positive rearrangement studies in the context of otherwise normal ancillary findings is unknown, and as such, we hypothesized that gene rearrangement studies may be predictive of an emerging B- or T-cell clone in the absence of other abnormal laboratory tests. Data from all patients who underwent IG or TCR gene rearrangement testing at the authors’ affiliated VA hospital between January 1, 2013, and July 6, 2018, were extracted from the electronic medical record. Date of testing; specimen source; and morphologic, flow cytometric, IHC, and cytogenetic characterization of the tissue source were recorded from pathology reports. Gene rearrangement results were categorized as true positive, false positive, false negative, or true negative. Lastly, patient records were reviewed for subsequent diagnosis of hematologic malignancy in patients with positive gene rearrangement results with negative ancillary testing. A total of 136 patients, who had 203 gene rearrangement studies (50 PB and 153 BM), were analyzed. In TCR studies, there were 2 false positives and 1 false negative in 47 PB assays, as well as 7 false positives and 1 false negative in 54 BM assays. Regarding IG studies, 3 false positives and 12 false negatives in 99 BM studies were identified. Sensitivity and specificity, respectively, were calculated for PB TCR studies (94% and 93%), BM IG studies (71% and 95%), and BM TCR studies (92% and 83%). Analysis of PB IG gene rearrangement studies was not performed due to the small number of tests (3; all true negative). None of the 12 patients with false-positive IG/TCR gene rearrangement studies later developed a lymphoproliferative disorder, although 2 patients were later diagnosed with acute myeloid leukemia. Of the 14 false negatives, 10 (71%) were related to a diagnosis of plasma cell neoplasms. Results from the present study suggest that positive IG/TCR gene rearrangement studies are not predictive of lymphoproliferative disorders in the context of otherwise negative BM or PB findings. As such, when faced with equivocal pathology reports, clinicians can be practically advised that isolated positive IG/TCR gene rearrangement results may not indicate the need for closer surveillance.

scholarly journals The evaluation of binary classification tasks in economical prediction

2010 ◽  
Vol 58 (6) ◽  
pp. 369-378
Author(s):  
Martin Pokorný
Keyword(s):  
False Positive ◽  
Roc Analysis ◽  
Binary Classification ◽  
False Negative ◽  
Optimal Threshold ◽  
False Negatives ◽  
Classifier Performance ◽  
False Negative Error ◽  
Classification Tasks ◽  
Error Costs

In the area of economical classification tasks, the accuracy maximization is often used to evaluate classifier performance. Accuracy maximization (or error rate minimization) suffers from the assumption of equal false positive and false negative error costs. Furthermore, accuracy is not able to express true classifier performance under skewed class distribution. Due to these limitations, the use of accuracy on real tasks is questionable. In a real binary classification task, the difference between the costs of false positive and false negative error is usually critical. To overcome this issue, the Receiver Ope­rating Characteristic (ROC) method in relation to decision-analytic principles can be used. One essential advantage of this method is the possibility of classifier performance visualization by means of a ROC graph. This paper presents concrete examples of binary classification, where the inadequacy of accuracy as the evaluation metric is shown, and on the same examples the ROC method is applied. From the set of possible classification models, the probabilistic classifier with continuous output is under consideration. Mainly two questions are solved. Firstly, the selection of the best classifier from a set of possible classifiers. For example, accuracy metric rates two classifiers almost equiva­lently (87.7 % and 89.3 %), whereas decision analysis (via costs minimization) or ROC analysis reveal differe­nt performance according to target conditions of unequal error costs of false positives and false negatives. Secondly, the setting of an optimal decision threshold at classifier’s output. For example, accuracy maximization finds the optimal threshold at classifier’s output in value of 0.597, but the optimal threshold respecting higher costs of false negatives is discovered by costs minimization or ROC analysis in a value substantially lower (0.477).

scholarly journals OPTIMIZATION OF CANCER DETECTION

1974 ◽  
Vol 22 (7) ◽  
pp. 663-667 ◽  
Author(s):  
DAN H. MOORE
Keyword(s):  
Cancer Detection ◽  
False Positive ◽  
False Negative ◽  
Optimal Number ◽  
False Negatives ◽  
Expected Cost ◽  
Predetermined Level ◽  
Diagnosis Of Cancer ◽  
Number Of Cells ◽  
Cost Equation

A statistical model is developed that describes the population of women who are given a cytologic screening test for cervical cancer. The model is used to determine false positive and false negative rates as a function of (a) the proportion of "positive" cells in women free from cancer and in those with cancer, (b) the number of cells examined and (c) the minimal number of positive cells for a diagnosis of cancer. The model allows estimation of the minimal number of cells that must be examined in order to reduce both the false positive and the false negative rates below some predetermined levels. An expected cost equation is derived which combines the costs of examining each cell with the costs for false positives and false negatives. It is shown how cancer detection can be optimized through the use of this cost equation. The method determines both the maximal permissible cost for examining each cell and the optimal number of cells to examine in order to reduce the over-all expected cost below some predetermined level.

scholarly journals Serum Thyroglobulin (Tg) Monitoring of Patients with Differentiated Thyroid Cancer Using Sensitive (Second-Generation) Immunometric Assays Can Be Disrupted by False-Negative and False-Positive Serum Thyroglobulin Autoantibody Misclassifications

2014 ◽  
Vol 99 (12) ◽  
pp. 4589-4599 ◽  
Author(s):  
Carole Spencer ◽  
Ivana Petrovic ◽  
Shireen Fatemi ◽  
Jonathan LoPresti
Keyword(s):  
Thyroid Cancer ◽  
False Positive ◽  
Differentiated Thyroid Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negatives ◽  
Serum Thyroglobulin ◽  
Positive Serum ◽  
Operating Characteristic Curve

Context: Reliable thyroglobulin (Tg) autoantibody (TgAb) detection before Tg testing for differentiated thyroid cancer (DTC) is critical when TgAb status (positive/negative) is used to authenticate sensitive second-generation immunometric assay (2GIMA) measurements as free from TgAb interference and when reflexing “TgAb-positive” sera to TgAb-resistant, but less sensitive, Tg methodologies (radioimmunoassay [RIA] or liquid chromatography-tandem mass spectrometry [LC-MS/MS]). Objective: The purpose of this study was to assess how different Kronus (K) vs Roche (R) TgAb method cutoffs for “positivity” influence false-negative vs false-positive serum TgAb misclassifications that may reduce the clinical utility of reflex Tg testing. Methods: Serum Tg2GIMA, TgRIA, and TgLC-MS/MS measurements for 52 TgAb-positive and 37 TgAb-negative patients with persistent/recurrent DTC were compared. A total of 1426 DTC sera with TgRIA of ≥1.0 μg/L had false-negative and false-positive TgAb frequencies determined using low Tg2GIMA/TgRIA ratios (<75%) to indicate TgAb interference. Results: TgAb-negative patients with disease displayed Tg2GIMA, TgRIA, and TgLC-MS/MS serum discordances (% coefficient of variation = 24 ± 20%, range, 0%–100%). Of the TgAb-positive patients with disease, 98% had undetectable/lower Tg2GIMA vs either TgRIA or TgLC-MS/MS (P < .01), whereas 8 of 52 (15%) had undetectable Tg2GIMA + TgLC-MS/MS associated with TgRIA of ≥1.0 μg/L. Receiver operating characteristic curve analysis reported more sensitivity for TgAb method K vs R (81.9% vs 69.1%, P < .001), but receiver operating characteristic curve cutoffs (>0.6 kIU/L [K] vs >40 kIU/L [R]) had unacceptably high false-negative frequencies (22%–32%), whereas false positives approximated 12%. Functional sensitivity cutoffs minimized false negatives (13.5% [K] vs 21.3% [R], P < .01) and severe interferences (Tg2GIMA, <0.10 μg/L) (0.7% [K] vs 2.4% [R], P < .05) but false positives approximated 23%. Conclusions: Reliable detection of interfering TgAbs is method and cutoff dependent. No cutoff eliminated both false-negative and false-positive TgAb misclassifications. Functional sensitivity cutoffs were optimal for minimizing false negatives but have inherent imprecision (20% coefficient of variation) that, exacerbated by TgAb biologic variability during DTC monitoring, could cause TgAb status to fluctuate for patients with low TgAb concentrations, prompting unnecessary Tg method changes and disrupting Tg monitoring. Laboratories using reflexing should limit Tg method changes by considering a patient's Tg + TgAb testing history in addition to current TgAb status before Tg method selection.

scholarly journals Assessing the Clinical Impact of Prostate-Specific Antigen Assay Variability and Nonequimolarity: A Simulation Study Based on the Population of the United Kingdom

2004 ◽  
Vol 50 (6) ◽  
pp. 1012-1016 ◽  
Author(s):  
Andrew W Roddam ◽  
Christopher P Price ◽  
Naomi E Allen ◽  
Anthony Milford Ward ◽  
Keyword(s):  
United Kingdom ◽  
Prostate Specific Antigen ◽  
False Positive ◽  
False Negative ◽  
Specific Antigen ◽  
False Positives ◽  
False Negatives ◽  
Worst Case ◽  
The United Kingdom ◽  
Analytical Imprecision

Abstract Background: Prostate-specific antigen (PSA) is the most widely used serum biomarker to differentiate between malignant and benign prostate disease. Assays that measure PSA can be biased and/or nonequimolar and hence report significantly different PSA values for samples with the same nominal amount. This report investigates the effects of biased and nonequimolar assays on the decision to recommend a patient for a prostate biopsy based on age-specific PSA values. Methods: A simulation model, calibrated to the distribution of PSA values in the United Kingdom, was developed to estimate the effects of bias, nonequimolarity, and analytical imprecision in terms of the rates of men who are recommended to have a biopsy on the basis of their assay-reported PSA values when their true PSA values are below the threshold (false positives) or vice versa (false negatives). Results: False recommendation rates for a calibrated equimolar assay are 0.5–0.9% for analytical imprecision between 5% and 10%. Positive bias leads to significant increases in false positives and significant decreases in false negatives, whereas negative bias has the opposite effect. False-positive rates for nonequimolar assays increase from 0.5% to 13% in the worst-case scenario, whereas false-negative rates are almost always 0%. Conclusions: Biased and nonequimolar assays can have major detrimental effects on both false-negative and false-positive rates for recommending biopsy. PSA assays should therefore be calibrated to the International Standards and be unbiased and equimolar in response to minimize the likelihood of incorrect clinical decisions, which are potentially detrimental for both patient and healthcare provider.

scholarly journals CORP: Minimizing the chances of false positives and false negatives

2017 ◽  
Vol 122 (1) ◽  
pp. 91-95 ◽  
Author(s):  
Douglas Curran-Everett
Keyword(s):  
Hypothesis Testing ◽  
Experimental Technique ◽  
False Positive ◽  
Scientific Discovery ◽  
False Negative ◽  
False Positives ◽  
Experimental Result ◽  
False Negatives ◽  
Scientific Equipment ◽  
Recent Initiative

Statistics is essential to the process of scientific discovery. An inescapable tenet of statistics, however, is the notion of uncertainty which has reared its head within the arena of reproducibility of research. The Journal of Applied Physiology’s recent initiative, “Cores of Reproducibility in Physiology,” is designed to improve the reproducibility of research: each article is designed to elucidate the principles and nuances of using some piece of scientific equipment or some experimental technique so that other researchers can obtain reproducible results. But other researchers can use some piece of equipment or some technique with expert skill and still fail to replicate an experimental result if they neglect to consider the fundamental concepts of statistics of hypothesis testing and estimation and their inescapable connection to the reproducibility of research. If we want to improve the reproducibility of our research, then we want to minimize the chance that we get a false positive and—at the same time—we want to minimize the chance that we get a false negative. In this review I outline strategies to accomplish each of these things. These strategies are related intimately to fundamental concepts of statistics and the inherent uncertainty embedded in them.

scholarly journals Predictors of dementia misclassification when using brief cognitive assessments

2018 ◽  
Vol 9 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Janice M. Ranson ◽  
Elżbieta Kuźma ◽  
William Hamilton ◽  
Graciela Muniz-Terrera ◽  
Kenneth M. Langa ◽  
...  
Keyword(s):  
Nursing Home ◽  
False Positive ◽  
Polynomial Regression ◽  
False Negative ◽  
False Positives ◽  
Cognitive Assessments ◽  
False Negatives ◽  
Cut Point ◽  
Home Residency ◽  
Poor Memory

BackgroundBrief cognitive assessments can result in false-positive and false-negative dementia misclassification. We aimed to identify predictors of misclassification by 3 brief cognitive assessments; the Mini-Mental State Examination (MMSE), Memory Impairment Screen (MIS) and animal naming (AN).MethodsParticipants were 824 older adults in the population-based US Aging, Demographics and Memory Study with adjudicated dementia diagnosis (DSM-III-R and DSM-IV criteria) as the reference standard. Predictors of false-negative, false-positive and overall misclassification by the MMSE (cut-point <24), MIS (cut-point <5) and AN (cut-point <9) were analysed separately in multivariate bootstrapped fractional polynomial regression models. Twenty-two candidate predictors included sociodemographics, dementia risk factors and potential sources of test bias.ResultsMisclassification by at least one assessment occurred in 301 (35.7%) participants, whereas only 14 (1.7%) were misclassified by all 3 assessments. There were different patterns of predictors for misclassification by each assessment. Years of education predicts higher false-negatives (odds ratio [OR] 1.23, 95% confidence interval [95% CI] 1.07–1.40) and lower false-positives (OR 0.77, 95% CI 0.70–0.83) by the MMSE. Nursing home residency predicts lower false-negatives (OR 0.15, 95% CI 0.03–0.63) and higher false-positives (OR 4.85, 95% CI 1.27–18.45) by AN. Across the assessments, false-negatives were most consistently predicted by absence of informant-rated poor memory. False-positives were most consistently predicted by age, nursing home residency and non-Caucasian ethnicity (all p < 0.05 in at least 2 models). The only consistent predictor of overall misclassification across all assessments was absence of informant-rated poor memory.ConclusionsDementia is often misclassified when using brief cognitive assessments, largely due to test specific biases.

Drug screening in urine by cloned enzyme donor immunoassay (CEDIA) and kinetic interaction of microparticles in solution (KIMS): a comparative study

2006 ◽  
Vol 44 (4) ◽  
Author(s):  
Lutz Schwettmann ◽  
Wolf-Rüdiger Külpmann ◽  
Christian Vidal
Keyword(s):  
Gas Chromatography ◽  
Drug Screening ◽  
False Positive ◽  
False Negative ◽  
False Positives ◽  
Gas Chromatography Mass Spectrometry ◽  
Electron Capture Detection ◽  
False Negatives ◽  
Negative Results ◽  
Kinetic Interaction

AbstractTwo commercially available drug-screening assays were evaluated: the Roche kinetic interaction of microparticles in solution (KIMS) assay and the Microgenics cloned enzyme donor immunoassay (CEDIA). Urine samples from known drug-abuse patients were analyzed for amphetamines, barbiturates, benzodiazepines, benzoylecgonine, cannabinoids, LSD, methadone and opiates. Samples with discordant findings for the two assays were analyzed by gas chromatography/mass spectrometry (GC/MS) or gas chromatography/electron capture detection (GC/ECD). Amphetamines showed 96.0% concordant results, with two false positive findings by CEDIA, three by KIMS and a further two false negatives by KIMS. Barbiturates showed 99.4% concordant results, with one false negative by KIMS. Benzodiazepines showed 97.4% concordant results, with two false negatives by KIMS (cutoff 100μg/L, CEDIA cutoff 300 μg/L). Benzoylecgonine showed 17.8% concordant positive and 82.2% concordant negative results and no false finding by either assay. Cannabinoids showed 99.3% concordant results, with one sample negative by KIMS at a cutoff of 50μg/L and positive by CEDIA (cutoff 25μg/L). For LSD, 6.7% of findings were not in agreement. Methadone showed 97.5% concordant results, with two false positives by CEDIA, and one false positive and one false negative by KIMS. Opiates showed 96.9% concordant results, with no false KIMS results, but four false positives by CEDIA. The results indicate that the agreement of the CEDIA and KIMS results for the eight drugs is rather good (93.3–100%).

scholarly journals Does it matter for the radiologists’ performance whether they read short or long batches in organized mammographic screening?

2021 ◽  
Author(s):  
Heinrich A. Backmann ◽  
Marthe Larsen ◽  
Anders S. Danielsen ◽  
Solveig Hofvind
Keyword(s):  
False Positive ◽  
False Negative ◽  
Population Level ◽  
Time Of Day ◽  
False Positives ◽  
Mediation Effect ◽  
False Negatives ◽  
Negatively Associated ◽  
The Individual ◽  
Image Position

Abstract Objective To analyze the association between radiologists’ performance and image position within a batch in screen reading of mammograms in Norway. Method We described true and false positives and true and false negatives by groups of image positions and batch sizes for 2,937,312 screen readings performed from 2012 to 2018. Mixed-effects models were used to obtain adjusted proportions of true and false positive, true and false negative, sensitivity, and specificity for different image positions. We adjusted for time of day and weekday and included the individual variation between the radiologists as random effects. Time spent reading was included in an additional model to explore a possible mediation effect. Result True and false positives were negatively associated with image position within the batch, while the rates of true and false negatives were positively associated. In the adjusted analyses, the rate of true positives was 4.0 per 1000 (95% CI: 3.8–4.2) readings for image position 10 and 3.9 (95% CI: 3.7–4.1) for image position 60. The rate of true negatives was 94.4% (95% CI: 94.0–94.8) for image position 10 and 94.8% (95% CI: 94.4–95.2) for image position 60. Per 1000 readings, the rate of false negative was 0.60 (95% CI: 0.53–0.67) for image position 10 and 0.62 (95% CI: 0.55–0.69) for image position 60. Conclusion There was a decrease in the radiologists’ sensitivity throughout the batch, and although this effect was small, our results may be clinically relevant at a population level or when multiplying the differences with the number of screen readings for the individual radiologists. Key Points • True and false positive reading scores were negatively associated with image position within a batch. • A decreasing trend of positive scores indicated a beneficial effect of a certain number of screen readings within a batch. • False negative scores increased throughout the batch but the association was not statistically significant.

scholarly journals Underperformance of Contemporary Phase III Oncology Trials and Strategies for Improvement

2021 ◽  
Vol 19 (9) ◽  
pp. 1072-1078
Author(s):  
Changyu Shen ◽  
Enrico G. Ferro ◽  
Huiping Xu ◽  
Daniel B. Kramer ◽  
Rushad Patell ◽  
...  
Keyword(s):  
Effect Size ◽  
False Positive ◽  
Statistical Significance ◽  
False Negative ◽  
False Positives ◽  
Statistical Testing ◽  
Phase Iii ◽  
False Negatives ◽  
Experimental Therapies ◽  
Phase Iii Trials

Background: Statistical testing in phase III clinical trials is subject to chance errors, which can lead to false conclusions with substantial clinical and economic consequences for patients and society. Methods: We collected summary data for the primary endpoints of overall survival (OS) and progression-related survival (PRS) (eg, time to other type of event) for industry-sponsored, randomized, phase III superiority oncology trials from 2008 through 2017. Using an empirical Bayes methodology, we estimated the number of false-positive and false-negative errors in these trials and the errors under alternative P value thresholds and/or sample sizes. Results: We analyzed 187 OS and 216 PRS endpoints from 362 trials. Among 56 OS endpoints that achieved statistical significance, the true efficacy of experimental therapies failed to reach the projected effect size in 33 cases (58.4% false-positives). Among 131 OS endpoints that did not achieve statistical significance, the true efficacy of experimental therapies reached the projected effect size in 1 case (0.9% false-negatives). For PRS endpoints, there were 34 (24.5%) false-positives and 3 (4.2%) false-negatives. Applying an alternative P value threshold and/or sample size could reduce false-positive errors and slightly increase false-negative errors. Conclusions: Current statistical approaches detect almost all truly effective oncologic therapies studied in phase III trials, but they generate many false-positives. Adjusting testing procedures in phase III trials is numerically favorable but practically infeasible. The root of the problem is the large number of ineffective therapies being studied in phase III trials. Innovative strategies are needed to efficiently identify which new therapies merit phase III testing.

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