<b>cutpointr</b>: Improved Estimation and Validation of Optimal Cutpoints in <i>R</i>

Thiele, Christian; Hirschfeld, Gerrit

doi:10.18637/jss.v098.i11

Cited by 256 publications

(185 citation statements)

References 24 publications

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“…Correlations were considered as follows: less than 0.2 negligible association, 0.2 to 0.29 weak association, 0.3 to 0.39 moderate association, 0.4 to 0.69 strong association, and greater than 0.7 very strong association [38]. The optimal cut-off point value of serum AMH, representing the value associated with the greatest summation of sensitivity and specificity to predict semen quality, was obtained using the package cutpointr after dichotomization of the data [39]. Different threshold values for dichotomization have been tested to find out which one had the best association with serum AMH.…”

Section: Statistical Analysesmentioning

confidence: 99%

Serum Anti-Müllerian Hormone: A Potential Semen Quality Biomarker in Stud Dogs?

Domain

Buczkowska

Kalak

et al. 2022

Animals

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Anti-Müllerian hormone (AMH) has been suggested to be involved in spermatogenesis. The aim of this study was to investigate the relationship between blood serum AMH concentration and semen quality in dogs. Moreover, this study sought to find the optimal cut-off point value of serum AMH with the greatest sensitivity and specificity to predict semen quality. Forty-five clinically healthy dogs were included in the study and their age as well as the following semen parameters were determined and correlated to serum AMH concentration: total sperm output, normal morphology, plasma membrane integrity, total motility, progressive motility, and velocity parameters. Statistical analysis for correlations were performed using Spearman’s correlation coefficients. Moderate negative associations were found between serum AMH and semen total motility (r = −0.38, p = 0.01), progressive motility (r = −0.36, p = 0.01), and normal morphology (r = −0.36, p= 0.02). Based on these associations, an AMH concentration of 5.54 µg/L was found to be the optimal cut-off point value to obtain the greatest summation of sensitivity (86%) and specificity (63%) to predict semen quality. The serum AMH assay may therefore be a potential hormonal marker to predict which dogs would require further semen analysis. Future research is however needed to confirm these preliminary results.

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Section: Statistical Analysesmentioning

confidence: 99%

Serum Anti-Müllerian Hormone: A Potential Semen Quality Biomarker in Stud Dogs?

Domain

Buczkowska

Kalak

et al. 2022

Animals

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“…For the primary analysis, we measured performance characteristics for DRS and GOSE cut-points for classifying participants meeting our reference standard criteria for FIM-dependency. For each cut-point, we computed classification performance measures, including area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), using 95% confidence intervals generated from 1000 bootstrapped samples, with performance assessed in the out-of-bag sample (R package: cutpointr) 39 . An applied definition of each performance measure is provided in Supplementary Table 1.…”

Section: Discussionmentioning

confidence: 99%

Comparison of common outcome measures for assessing independence in patients diagnosed with disorders of consciousness: A Traumatic Brain Injury Model Systems Study

Snider

Kowalski

Izzy

et al. 2022

Preprint

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Importance Patients with disorders of consciousness (DoC) after traumatic brain injury (TBI) recover to varying degrees of functional dependency. Dependency is difficult to measure but critical for outcome interpretation and prognostic counseling. Traditional outcome measures, like the Glasgow Outcome Scale-Extended (GOSE), are mandated by the US Food and Drug Administration for evaluating TBI clinical trial efficacy but have an unknown accuracy for measuring dependency. Objective We used the Functional Independence Measure (FIM) as the reference standard to evaluate how accurately the GOSE and Disability Rating Scale (DRS) assess functional dependency in the worlds largest cohort of patients with DoC after TBI. We propose an alternate, data-driven, approach to measuring dependency. Design, Setting, and Participants In this cohort study, we included patients with DoC prospectively enrolled in the longitudinal Traumatic Brain Injury Model Systems National Database (TBIMS NDB). Participants were survivors of moderate/severe TBI with DoC on admission to a US inpatient rehabilitation center between 1988 and 2020, followed 1 year after injury. Exposures We examined the classification performance of common TBI outcome measure cut-points (GOSE <=3 and <=4 [Lower and Upper Severe Disability, respectively], and DRS >=12 [Severe Disability]) in identifying subjects with functional dependency at 1 year. We compared data-derived optimal cut-points on these scales to a novel DRS-based marker of dependency, the DRSDepend. Main Outcome and Measure Total FIM score < 80 (FIM-dependency) at 1 year. Results Of 18,486 TBIMS participants, 1,483 with DoC on arrival to inpatient rehabilitation met inclusion criteria (mean [SD] age=38 [18] years; 76% male). The sensitivity of GOSE cut-points of <=3 and <=4 for identifying FIM-dependency were 97% and 98%, but specificities were 73% and 51%, respectively. The sensitivity of the DRS cut-point of >=12 was 60%, but specificity was 100%. The DRSDepend had a sensitivity of 83% and a specificity of 94% for classifying FIM-dependency, with a greater AUROC than the data-derived optimal GOSE (<=3, p=0.01) and DRS (>=10, p=0.008) cut-points. Conclusions and Relevance Commonly-used GOSE and DRS cut-points have limited sensitivity or specificity for identifying functional dependency. The DRSDepend identifies FIM-dependency more accurately than GOSE and DRS cut-points, but requires further validation.

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“…The sensitivity, specificity, accuracy, and AUC were calculated for each sample percentile. The genes with the highest sensitivity and specificity were initially selected [ 34 ]. This group was further categorized based on the expression levels at the various percentiles.…”

Section: Methodsmentioning

confidence: 99%

Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning

Satter

Tran

et al. 2022

Cells

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Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

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cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R

Cited by 256 publications

References 24 publications

Serum Anti-Müllerian Hormone: A Potential Semen Quality Biomarker in Stud Dogs?

Serum Anti-Müllerian Hormone: A Potential Semen Quality Biomarker in Stud Dogs?

Comparison of common outcome measures for assessing independence in patients diagnosed with disorders of consciousness: A Traumatic Brain Injury Model Systems Study

Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning

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