arXiv subjects

Lingli Yang

Publications and source records attributed to Lingli Yang.

5 recordsLinked to original sources

arXiv2026

Worst-Case Win Ratios Under Partially Specified Outcome Hierarchies

Win statistics require a prespecified outcome hierarchy. Clinical teams sometimes agree only on the highest priority outcome, leaving the order of lower priority outcomes unresolved, and clinically meaningful thresholds may be specified as ranges. Separate sensitivity analyses describe how the results change. A single inference for the full set of planned analyses is generally absent. We define the estimand as the smallest net benefit among the comparison rules allowed by the protocol or statistical analysis plan. Each rule uses the usual two-sample U-statistic. Large-sample results are developed for a finite list of rules and for a continuous threshold range. Inversion of an intersection-union test gives a one-sided lower confidence bound, with no multiplicity adjustment for the single claim that every individual net benefit is positive. Simulations show valid one-sided coverage and illustrate the gap between a favorable result for one selected hierarchy and a favorable result across all prespecified hierarchies. An application to ACTG 175 shows how the ordering of laboratory outcomes can affect the strength of the conclusion. A further example identifies an unfavorable threshold that is missed by a sparse grid.

stat.ME↗

arXiv2024

Detection of LUAD-Associated Genes Using Wasserstein Distance in Multi-Omics Feature Selection

Lung adenocarcinoma (LUAD) is characterized by substantial genetic heterogeneity, posing challenges in identifying reliable biomarkers for improved diagnosis and treatment. Tumor Mutational Burden (TMB) has traditionally been regarded as a predictive biomarker, given its association with immune response and treatment efficacy. In this study, we treated TMB as a response variable to identify genes highly correlated with it, aiming to understand its genetic drivers. We conducted a thorough investigation of recent feature selection methods through extensive simulations, selecting PC-Screen, DC-SIS, and WD-Screen as top performers. These methods handle multi-omics structures effectively, and can accommodate both categorical and continuous data types at the same time for each gene. Using data from The Cancer Genome Atlas (TCGA) via cBioPortal, we combined copy number alteration (CNA), mRNA expression and DNA methylation data as multi-omics predictors and applied these methods, selecting genes consistently identified across all three methods. 13 common genes were identified, including HSD17B4, PCBD2, which show strong associations with TMB. Our multi-omics strategy and robust feature selection approach provide insights into the genetic determinants of TMB, with implications for targeted LUAD therapies.

stat.AP↗

arXiv2023

Multivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach

Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other existing approaches that rely on pairwise comparisons for each individual endpoint, the proposed method directly incorporates the multivariate ranks of the observations. By considering the joint ranking of all endpoints, the proposed approach provides robustness against diverse data distributions and censoring mechanisms commonly encountered in clinical trials. Through extensive simulations, we demonstrate the superior performance of the multivariate rank-based approach in controlling type I error and achieving higher power compared to existing rank-based methods. The simulations illustrate the advantages of leveraging multivariate ranks and highlight the robustness of the approach in various settings. The proposed method offers an effective tool for the analysis of multiple endpoints in clinical trials, enhancing the reliability and efficiency of outcome evaluations.

stat.ME↗

arXiv2023

On the Confidence Intervals in Bioequivalence Studies

A bioequivalence study is a type of clinical trial designed to compare the biological equivalence of two different formulations of a drug. Such studies are typically conducted in controlled clinical settings with human subjects, who are randomly assigned to receive two formulations. The two formulations are then compared with respect to their pharmacokinetic profiles, which encompass the absorption, distribution, metabolism, and elimination of the drug. Under the guidance from Food and Drug Administration (FDA), for a size-$\alpha$ bioequivalence test, the standard approach is to construct a $100(1-2\alpha)\%$ confidence interval and verify if the confidence interval falls with the critical region. In this work, we clarify that $100(1-2\alpha)\%$ confidence interval approach for bioequivalence testing yields a size-$\alpha$ test only when the two one-sided tests in TOST are ``equal-tailed''. Furthermore, a $100(1-\alpha)\%$ confidence interval approach is also discussed in the bioequivalence study.

stat.ME↗

arXiv2022

Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-dimensional, low-sample-size data. This approach first extracts a low-dimensional representation of input data and then applies feature screening based on multivariate rank distance correlation recently developed by Deb and Sen (2021). This approach combines the strengths of both deep neural networks and feature screening, and thereby has the following appealing features in addition to its ability of handling ultra high-dimensional data with small number of samples: (1) it is model free and distribution free; (2) it can be used for both supervised and unsupervised feature selection; and (3) it is capable of recovering the original input data. The superiority of DeepFS is demonstrated via extensive simulation studies and real data analyses.

stat.ML↗