arXiv subjects

Yan Yuan

Publications and source records attributed to Yan Yuan.

7 recordsLinked to original sources

arXiv2026

Age-Specific Logistic Regression with Complex Event Time Data

In attempt to advance the current practice for assessing and predicting the primary ovarian insufficiency (POI) risk in female childhood cancer survivors, we propose two estimating function based approaches for age-specific logistic regression. Both approaches adapt the inverse probability of censoring weighting (IPCW) strategy and yield consistent estimators with asymptotic normality. The first approach modifies the IPCW weights used by Im et al. (2023) to account for doubly censoring. The second approach extends the outcome weighted IPCW approach to use the information of the subjects censored before the analysis time. We consider variance estimation for the estimators and explore by simulation the two approaches implemented in the situations where the conditional right-censoring time distribution required in the IPCW weighs is unknown and approximated using the survival random forest approaches, stratified empirical distribution functions, or the estimator under the Cox proportional hazards model. The numerical studies indicate that the second approach is more efficient when right-censoring is relatively heavy, whereas the first approach is preferable when the right-censoring is light. We also observe that the performance of the two approaches heavily relies on the estimation of censoring distribution in our simulation settings. The POI data from a childhood cancer survivor study are employed throughout the paper for motivation and illustration. Our data analysis provides new insight into understanding the POI risk among cancer survivors.

stat.ME↗

arXiv2024

A Dual-View Approach to Classifying Radiology Reports by Co-Training

Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods.

cs.CL↗

arXiv2024

Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution

Previous approaches for blind image super-resolution (SR) have relied on degradation estimation to restore high-resolution (HR) images from their low-resolution (LR) counterparts. However, accurate degradation estimation poses significant challenges. The SR model's incompatibility with degradation estimation methods, particularly the Correction Filter, may significantly impair performance as a result of correction errors. In this paper, we introduce a novel blind SR approach that focuses on Learning Correction Errors (LCE). Our method employs a lightweight Corrector to obtain a corrected low-resolution (CLR) image. Subsequently, within an SR network, we jointly optimize SR performance by utilizing both the original LR image and the frequency learning of the CLR image. Additionally, we propose a new Frequency-Self Attention block (FSAB) that enhances the global information utilization ability of Transformer. This block integrates both self-attention and frequency spatial attention mechanisms. Extensive ablation and comparison experiments conducted across various settings demonstrate the superiority of our method in terms of visual quality and accuracy. Our approach effectively addresses the challenges associated with degradation estimation and correction errors, paving the way for more accurate blind image SR.

eess.IV↗

arXiv2020

Is the new model better? One metric says yes, but the other says no. Which metric do I use?

Incremental value (IncV) evaluates the performance change from an existing risk model to a new model. It is one of the key considerations in deciding whether a new risk model performs better than the existing one. Problems arise when different IncV metrics contradict each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of conflicting conclusions is not uncommon, and it creates a dilemma in medical decision making. In this article, we examine the analytical connections and differences between two IncV metrics: IncV in AUC (IncV-AUC) and IncV in AP (IncV-AP). Additionally, since they are both semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (IncV-sBrS), via a numerical study. We demonstrate that both IncV-AUC and IncV-AP are weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, IncV-AP assigns heavier weights to the changes in the high-risk group, whereas IncV-AUC weights the changes equally. In the numerical study, we find that IncV-AP has a wide range, from negative to positive, but the size of IncV-AUC is much smaller. In addition, IncV-AP and IncV-sBr Sare highly consistent, but IncV-AUC is negatively correlated with IncV-sBrS and IncV-AP at a low event rate. IncV-AUC and IncV-AP are the least consistent among the three pairs, and their differences are more pronounced as the event rate decreases.

stat.ME↗

arXiv2016

A Threshold-free Prospective Prediction Accuracy Measure for Censored Time to Event Data

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a pre-specified future time $t_0$. Prospective accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cut-off value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between competing risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing two risk score systems for predicting heart failure in childhood cancer survivors.

stat.ME↗

arXiv2016

A Two-Stage Patient-Focused Study Design for Rare Disease Controlled Trials

We developed a study design for rare disease clinical trials (RDTs) that efficiently evaluate treatments, promotes access to new treatments during treatment development, and optimizes healthcare resource utilization for future treatment allocation, development, and prioritization. Comprehensive literature review and focus group discussion were conducted. To address the multifaceted challenges facing RDTs, four key issues for RDTs must be addressed, which are 1) the opportunity to access the new treatment; 2) assessment of outcomes where clinically validated outcomes may be lacking; 3) patient heterogeneity; and 4) duration of the study and number of patients required. Our proposed study design has two stages. Stage 1 distinguishes patients who respond to the treatment from those who do not respond to the treatment after assigning them all to the experimental treatment. Stage 2 evaluates the treatment effect comparatively among patients responded in Stage 1. In addition to treatment effect evaluation, our design can greatly benefit rare disease patients and clinical practice by increasing opportunities to access experimental treatments and by providing relevant information that can be used for tailoring treatments to certain subgroups, aiding future research in treatment development, and improving healthcare resource utilization.

stat.AP↗

arXiv2013

Threshold-free Evaluation of Medical Tests for Classification and Prediction: Average Precision versus Area Under the ROC Curve

When evaluating medical tests or biomarkers for disease classification, the area under the receiver-operating characteristic (ROC) curve is a widely used performance metric that does not require us to commit to a specific decision threshold. For the same type of evaluations, a different metric known as the average precision (AP) is used much more widely in the information retrieval literature. We study both metrics in some depths in order to elucidate their difference and relationship. More specifically, we explain mathematically why the AP may be more appropriate if the earlier part of the ROC curve is of interest. We also address practical matters, deriving an expression for the asymptotic variance of the AP, as well as providing real-world examples concerning the evaluation of protein biomarkers for prostate cancer and the assessment of digital versus film mammography for breast cancer screening.

stat.ME↗