arXiv subjects

Jiren Sun

Publications and source records attributed to Jiren Sun.

6 recordsLinked to original sources

arXiv2026

Empirical Simulation of Survival and Mixed-Type Data for Clinical Trial Design

Simulating realistic time-to-event data is essential for planning and evaluating complex clinical trial designs. Conventional approaches often sample event times from parametric families, such as Weibull or log-normal distributions, which restrict hazard shapes and may poorly represent observed survival data. We propose an empirical copula-based framework for simulating multivariate data containing continuous, binary, count, and right-censored time-to-event variables. The method completes censored historical survival data using a two-zone procedure that combines conditional Kaplan-Meier imputation with a parametric tail. It matches a target survival distribution through a log-scale location-scale transformation and a power distortion of the empirical percentile function, while preserving historical dependence through a Gaussian copula fitted to rank correlations. In an oncology trial of previously treated non-small-cell lung cancer, the method reconstructs overall survival and progression-free survival curves for the experimental arm using control-arm data and a small set of target percentiles. Simulations preserve rank correlations among baseline covariates and the dependence between progression-free and overall survival, with censored Kendall's tau of 0.522 compared with 0.549 in the observed data. The method is implemented in the R package EmpiricalSim.

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arXiv2026

Capturing Cumulative Disease Burden in Chronic Kidney Disease Outcome Trials: Area Under the Curve and Restricted Mean Time in Favor of Treatment Beyond Conventional Time-to-First Analysis

Chronic kidney disease (CKD) affects millions worldwide and progresses irreversibly through stages culminating in end-stage renal disease (ESRD) and death. Outcome trials in CKD traditionally employ time-to-first-event analyses using the Cox models. However, this approach has fundamental limitations for progressive diseases: it assigns equal weight to each composite endpoint component despite clear clinical hierarchy: an eGFR decline threshold receives the same weight as ESRD or death in the analysis, and it captures only the first occurrence while ignoring subsequent progression. Given CKD's gradual evolution over years, comprehensive treatment evaluation requires quantifying cumulative disease burden: integrating both event severity and time spent in each disease state. We propose two complementary approaches to better characterize treatment benefits by incorporating event severity and state occupancy: area under the curve (AUC) and restricted mean time in favor of treatment (RMT-IF). The AUC method assigns ordinal severity scores to disease states and calculates the area under the mean cumulative score curve, quantifying total event-free time lost. Treatment effects are expressed as AUC ratios or differences. The RMT-IF extends restricted mean survival time to multistate processes, measuring average time patients in the treatment arm spend in more favorable states versus the comparator. These methods better capture CKD's progressive nature where treatment benefits extend beyond first-event delay to overall disease trajectory modification. By discriminating between events of differing clinical importance and quantifying the complete disease course, these estimands offer alternative assessment frameworks for kidney-protective therapies, potentially improving efficiency and interpretability of future CKD outcome trials.

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arXiv2026

An Empirical Method for Analyzing Count Data

Count endpoints are common in clinical trials, particularly for recurrent events such as hypoglycemia. When interest centers on comparing overall event rates between treatment groups, negative binomial (NB) regression is widely used because it accommodates overdispersion and requires only event counts and exposure times. However, NB regression can be numerically unstable when events are sparse, and the efficiency gains from baseline covariate adjustment may be sensitive to model misspecification. We propose an empirical method that targets the same marginal estimand as NB regression -- the ratio of marginal event rates -- while avoiding distributional assumptions on the count outcome. Simulation studies show that the empirical method maintains appropriate Type I error control across diverse scenarios, including extreme overdispersion and zero inflation, achieves power comparable to NB regression, and yields consistent efficiency gains from baseline covariate adjustment. We illustrate the approach using severe hypoglycemia data from the QWINT-5 trial comparing insulin efsitora alfa with insulin degludec in adults with type 1 diabetes. In this sparse-event setting, the empirical method produced stable marginal rate estimates and rate ratios closely aligned with observed rates, while NB regression exhibited greater sensitivity and larger deviations from the observed rates in the sparsest intervals. The proposed empirical method provides a robust and numerically stable alternative to NB regression, particularly when the number of events is low or when numerical stability is a concern.

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arXiv2025

Inverse Probability Weighting for Recurrent Event Models

Recurrent events are common and important clinical trial endpoints in many disease areas, e.g., cardiovascular hospitalizations in heart failure, relapses in multiple sclerosis, or exacerbations in asthma. During a trial, patients may experience intercurrent events, that is, events after treatment assignment which affect the interpretation or existence of the outcome of interest. In many settings, a treatment effect in the scenario in which the intercurrent event would not occur is of clinical interest. A proper estimation method of such a hypothetical treatment effect has to account for all confounders of the recurrent event process and the intercurrent event. In this paper, we propose estimators targeting hypothetical estimands in recurrent events with proper adjustments of baseline and internal time-varying covariates. Specifically, we apply inverse probability weighting (IPW) to the commonly used Lin-Wei-Yang-Ying (LWYY) and negative binomial (NB) models in recurrent event analysis. Simulation studies demonstrate that our approach outperforms alternative analytical methods in terms of bias and power.

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arXiv2025

On the Proportional Principal Stratum Hazards Model

In clinical trials involving both mortality and morbidity, an active treatment can influence the observed risk of the first non-fatal event either directly, through its effect on the underlying non-fatal event process, or indirectly, through its effect on the death process, or both. Discerning the direct effect of treatment on the underlying first non-fatal event process holds clinical interest. However, with the competing risk of death, the Cox proportional hazards model that treats death as non-informative censoring and evaluates treatment effects on time to the first non-fatal event provides an estimate of the cause-specific hazard ratio, which may not correspond to the direct effect. To obtain the direct effect on the underlying first non-fatal event process, within the principal stratification framework, we define the principal stratum hazard and introduce the Proportional Principal Stratum Hazards model. This model estimates the principal stratum hazard ratio, which reflects the direct effect on the underlying first non-fatal event process in the presence of death and simplifies to the hazard ratio in the absence of death. The principal stratum membership is identified probabilistically using the shared frailty model, which assumes independence between the first non-fatal event process and the potential death processes, conditional on per-subject random frailty. Simulation studies are conducted to verify the reliability of our estimators. We illustrate the method using the Carvedilol Prospective Randomized Cumulative Survival trial, which involves heart-failure events.

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arXiv2024

Improve the Precision of Area Under the Curve Estimation for Recurrent Events Through Covariate Adjustment

The area under the curve (AUC) of the mean cumulative function (MCF) has recently been introduced as a novel estimand for evaluating treatment effects in recurrent event settings, offering an alternative to the commonly used Lin-Wei-Yang-Ying (LWYY) model. The AUC of the MCF provides a clinically interpretable summary measure that captures the overall burden of disease progression, regardless of whether the proportionality assumption holds. To improve the precision of the AUC estimation while preserving its unconditional interpretability, we propose a nonparametric covariate adjustment approach. This approach guarantees efficiency gain compared to unadjusted analysis, as demonstrated by theoretical asymptotic distributions, and is universally applicable to various randomization schemes, including both simple and covariate-adaptive designs. Extensive simulations across different scenarios further support its advantage in increasing statistical power. Our findings highlight the importance of covariate adjustment for the analysis of AUC in recurrent event settings, offering practical guidance for its application in randomized clinical trials.

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