arXiv subjects

Ruosha Li

Publications and source records attributed to Ruosha Li.

5 recordsLinked to original sources

arXiv2025

Predictiveness Curve Assessment under Competing Risks for Risk Prediction Models

The predictiveness curve is a valuable tool for predictive evaluation, risk stratification, and threshold selection in a target population, given a single biomarker or a prediction model. In the presence of competing risks, regression models are often used to generate predictive risk scores or probabilistic predictions targeting the cumulative incidence function--distinct from the cumulative distribution function used in conventional predictiveness curve analyses. We propose estimation and inference procedures for the predictiveness curve with a competing risks regression model, to display the relationship between the cumulative incidence probability and the quantiles of model-based predictions. The estimation procedure combines cross-validation with a flexible regression model for tau-year event risk given the model-based risk score, with corresponding inference procedures via perturbation resampling. The proposed methods perform satisfactorily in simulation studies and are implemented through an R package. We apply the proposed methods to a cirrhosis study to depict the predictiveness curve with model-based predictions for liver-related mortality.

stat.ME↗

arXiv2024

Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review

Objectives: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysis were extracted, such as covariates, matching algorithm, and reporting of estimated treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%) case-control studies were found among 171 identified articles. Forty-five studies (26.32%) provided a reasonable justification for covariates selection. One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide the model that was used for calculating the propensity score or did not report the matching algorithm, respectively. Seventy-three (42.69%) studies reported the method(s) for checking covariates balance. Forty studies (23.39%) had a statistician co-author. All the case-control studies (n=13) did not have a statistician co-author (p=0.006) and all studies that clarified the treatment effect estimation (n=6) had a statistician co-author (p<0.001). Conclusions: The reporting quality of the PSM analysis is suboptimal in some COVID-19 epidemiological studies. Some pitfalls may undermine study findings that involve PSM analysis, such as a mismatch between PSM analysis and study design.

q-bio.OT↗

arXiv2020

Quantile regression on inactivity time

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.

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arXiv2019

Dynamic Prediction of Competing Risk Events using Landmark Sub-distribution Hazard Model with Multiple Longitudinal Biomarker

The cause-specific cumulative incidence function (CIF) quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted CIF by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each landmark time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted CIF. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.

q-bio.QM↗

arXiv2018

False Discovery Rate Control for High-Dimensional Networks of Quantile Associations Conditioning on Covariates

Motivated by the gene co-expression pattern analysis, we propose a novel sample quantile-based contingency (squac) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple testing procedure based on squac to simultaneously test the independence between one pair of variables conditioning on covariates for all $p(p-1)/2$ pairs. Here, $p$ is the length of the outcomes and could exceed the sample size. The testing procedure does not require resampling or perturbation, and thus is computationally efficient. We prove by theory and numerical experiments that this testing method asymptotically controls the false discovery rate (\FDR). It outperforms all alternative methods when the complex association panterns exist. Applied to a gastric cancer data, this testing method successfully inferred the gene co-expression networks of early and late stage patients. It identified more changes in the networks which are associated with cancer survivals. We extend our method to the case that both the length of the outcomes and the length of covariates exceed the sample size, and show that the asymptotic theory still holds.

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