ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates

Rodríguez‐Álvarez, María Xosé; Inácio, Vanda

doi:10.32614/rj-2021-066

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…We also present the logit transformation‐based confidence interval, which is obtained by backtransforming the Wald‐type confidence interval of the logit‐transformed ϕ 0 . As a comparison, we present the results using the seimiparametric method by Janes and Pepe (2009) (Column “JP‐SP”), implemented in R/CRAN package ROCnReg (Rodríguez‐Álvarez and Inacio, 2020). All results are summarized over 5000 Monte Carlo datasets.…”

Section: Simulationsmentioning

confidence: 99%

Covariate adjustment in continuous biomarker assessment

Huang

Patil

et al. 2021

Biometrics

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Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate-specific control of the sensitivity. Variance estimates are obtained from a sample-based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate-adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer.

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Section: Simulationsmentioning

confidence: 99%

Covariate adjustment in continuous biomarker assessment

Huang

Patil

et al. 2021

Biometrics

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“…Such a bias decreases, however, when sample size increases. To investigate whether this bias could be due to the prior information used, we have conducted a sensitivity analysis with a data‐driven prior 35 (p. 532) and the results, not shown, were unchanged. In turn, the

{\mathrm{OVL}}_{\mathrm{DPM}\hbox{-} \mathrm{BB}}

estimator provides unbiased estimates in almost all cases considered; the only exception is in Scenario III when the underlying true value of the overlap coefficient is very high and the sample size is small (eg,

\left({n}_{\overline{D}},{n}_D\right)=\left(50,50\right)

).…”

Section: Resultsmentioning

confidence: 99%

Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis

Inácio

Guillén

2022

Statistics in Medicine

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Diagnostic tests play an important role in medical research and clinical practice. The ultimate goal of a diagnostic test is to distinguish between diseased and nondiseased individuals and before a test is routinely used in practice, it is a pivotal requirement that its ability to discriminate between these two states is thoroughly assessed. The overlap coefficient, which is defined as the proportion of overlap area between two probability density functions, has gained popularity as a summary measure of diagnostic accuracy. We propose two Bayesian nonparametric estimators, based on Dirichlet process mixtures, for estimating the overlap coefficient. We further introduce the covariate-specific overlap coefficient and develop a Bayesian nonparametric approach based on Dirichlet process mixtures of additive normal models for estimating it. A simulation study is conducted to assess the empirical performance of our proposed estimators.Two illustrations are provided: one concerned with the search for biomarkers of ovarian cancer and another one aimed to assess the age-specific accuracy of glucose as a biomarker of diabetes.

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“…For each PheRS, we assessed the following performance measures relative to the PASC status: (1) overall performance with Nagelkerke’s pseudo-R 2 using R packages “rcompanion” [ 45 ], (2) accuracy with Brier score using R package “DescTools” [ 46 ]; and (3) ability to discriminate between PASC cases and matched controls as measured by the area under the covariate-adjusted receiver operating characteristic (AROC; semiparametric frequentist inference) curve (denoted AAUC) using R package “ROCnReg” [ 47 ]. Firth’s bias reduction method was used to resolve the problem of separation in logistic regression (R package “brglm2”) [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US

Fritsche

Jin

Admon

et al. 2023

JCM

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Background: A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by post-acute sequelae of SARS CoV-2 infection (PACS). Using electronic health record data, we aimed to characterize PASC-associated diagnoses and develop risk prediction models. Methods: In our cohort of 63,675 patients with a history of COVID-19, 1724 (2.7%) had a recorded PASC diagnosis. We used a case–control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into phenotype risk scores (PheRSs) and evaluated their predictive performance. Results: In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and sixty-nine phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the cohort with a history of COVID-19 with a 3.5-fold increased risk (95% CI: 2.19, 5.55) for PASC compared to the bottom 50%. Conclusions: The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with potential for risk stratification approaches.

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ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates

Cited by 17 publications

References 33 publications

Covariate adjustment in continuous biomarker assessment

Covariate adjustment in continuous biomarker assessment

Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis

Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US

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