arXiv subjects

Ainesh Sewak

Publications and source records attributed to Ainesh Sewak.

3 recordsLinked to original sources

arXiv2025

Likelihood-based Modeling of Covariate-Specific Time-Dependent ROC Curves

Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it difficult to account for patient heterogeneity. In amyotrophic lateral sclerosis (ALS), factors such as age, site of onset and genetic status influence both survival and biomarker levels, yet their impact on the prognostic accuracy of biomarkers over time remains unclear. While time-dependent receiver operating characteristic methods have been developed to handle censored time-to-event outcomes, most do not adjust for covariates. To address this, we propose the nonparanormal prognostic biomarker framework, which models the joint distribution of the biomarker and event time while accounting for covariates. This allows estimation of covariate-specific time-dependent ROC curves and related summary measures. We apply the NPB framework to evaluate serum neurofilament light as a prognostic biomarker in ALS, showing that its accuracy varies over time and with patient characteristics. By capturing these covariate-specific effects, the NPB framework supports more targeted risk stratification and can potentially improve the design of clinical trials for new ALS treatments.

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arXiv2025

Transformation Discriminant Analysis for Constructing Optimal Biomarker Combinations

Accurate diagnostic tests are essential for effective screening and treatment. However, individual biomarkers often fail to provide sufficient diagnostic accuracy, as they typically capture only one aspect of the complex disease process. Combining multiple biomarkers, each capturing a distinct mechanism, can help constructing more informative diagnostic tests. In practice, logistic regression is used as the default to combine biomarkers, but it can perform poorly when biomarker distributions exhibit skewness or differ across disease groups. Nonparametric methods provide more flexibility but generally require large sample sizes that are infrequently available in biomedical research. We propose a novel framework called transformation discriminant analysis which combines biomarkers through the likelihood ratio function to construct theoretically optimal diagnostic scores. Transformation discriminant analysis balances between flexibility and efficiency. It can accommodate a wide range of distributional shapes and disease-specific dependence structures while remaining fully parametric. This allows for likelihood inference and strong performance even in small-sample settings. We evaluate TDA through simulations and benchmark its performance against commonly used methods. Finally, we illustrate its utility in constructing an optimal diagnostic test for hepatocellular carcinoma, a disease with no single ideal biomarker. An open-source R implementation is provided for reproducibility and broader application.

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arXiv2022

Transformation models for ROC analysis

Receiver operating characteristic (ROC) analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating ROC curves and its associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference whilst handling the complexities associated with medical data. Such complexities might include covariates that influence the diagnostic potential of a test, ordinal test data, censored data due to instrument detection limits or correlated biomarkers. We propose a regression model for the transformed test results which exploits the invariance of ROC curves to monotonic transformations and naturally accommodates these features. Our use of maximum likelihood inference guarantees asymptotic efficiency of the resulting estimators and associated confidence intervals. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the "tram" R package.

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