arXiv subjects

Emily Ouyang

Publications and source records attributed to Emily Ouyang.

3 recordsLinked to original sources

arXiv2026

FastJM: An R Package for Efficient Implementation of Semiparametric Joint Models for Longitudinal and Survival Data

Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. However, fitting joint models can be computationally challenging for large-scale and complex biomedical data. This paper introduces the \proglang{R} package \pkg{FastJM}, which provides computationally efficient frequentist estimation for three classes of semiparametric joint models: joint models with a single longitudinal biomarker, joint models with multiple longitudinal biomarkers, and joint models with a single longitudinal biomarker with heterogeneous within-subject (WS) variability. Within an expectation--maximization framework, \pkg{FastJM} employs customized linear-scan algorithms to efficiently update the nonparametric baseline hazards, thereby addressing a major computational bottleneck in semiparametric joint modeling. The package also supports commonly used time-dependent latent association structures by integrating these algorithms with a landmark multivariate joint modeling framework. \pkg{FastJM} provides a unified interface for model specification, estimation, inference, visualization, dynamic prediction, and prediction performance assessment, including cross-validated time-dependent accuracy measures and time-independent concordance statistics. We describe the underlying methodology and software implementation and demonstrate the main functionality of \pkg{FastJM} through reproducible examples.

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arXiv2025

Efficient Implementation of a Semiparametric Joint Model for Multivariate Longitudinal Biomarkers and Competing Risks Time-to-Event Data

Joint modeling has become increasingly popular for characterizing the association between one or more longitudinal biomarkers and competing risks time-to-event outcomes. However, semiparametric multivariate joint modeling for large-scale data encounter substantial statistical and computational challenges, primarily due to the high dimensionality of random effects and the complexity of estimating nonparametric baseline hazards. These challenges often lead to prolonged computation time and excessive memory usage, limiting the utility of joint modeling for biobank-scale datasets. In this article, we introduce an efficient implementation of a semiparametric multivariate joint model, supported by a normal approximation and customized linear scan algorithms within an expectation-maximization (EM) framework. Our method significantly reduces computation time and memory consumption, enabling the analysis of data from thousands of subjects. The scalability and estimation accuracy of our approach are demonstrated through two simulation studies. We also present an application to the Primary Biliary Cirrhosis (PBC) dataset involving five longitudinal biomarkers as an illustrative example. A user-friendly R package, \texttt{FastJM}, has been developed for the shared random effects joint model with efficient implementation. The package is publicly available on the Comprehensive R Archive Network: https://CRAN.R-project.org/package=FastJM.

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arXiv2023

Principles of Conditionality and Layering of Error Rates with Application to Platform Trials

There has been a misconception that only one type of error rate control is necessary in clinical trials, leading to debates over whether to prioritize Familywise Error Rate (FWER) or False Discovery Rate (FDR). This misconception has led to misleading statements about FWER control and proposals to shift towards FDR control, which could be manipulated by the industry. In reality, since the early 2000s, biopharmaceutical statistics have implicitly applied two layers of Type I error rate control. This aligns with Tukey's 1953 invention of Error Rate per Family (ERpF) for controlling error across studies, while FWER applies within each study. Our paper clarifies this layering, using Platform trials to demonstrate the verifiable conditions needed across studies for the FDA to fulfill its regulatory mission. We show that controlling FWER within a study at $5\%$ inherently controls ERpF across studies at 5-per-100, regardless of study correlations. This supports current regulatory practices that protect public health while fostering innovation. We also address concerns about ERpF stability in Platform trials, where shared controls introduce dependencies. By applying the Conditionality Principle and utilizing an innovative Shiny app, we explore how correlations impact ERpF variability, providing deeper insights for informed decision-making. Our findings, supported by principles like Layering of Error Rate Controls and the Conditionality Principle, are particularly relevant as Platform trials gain popularity for their efficiency in testing multiple treatments simultaneously.

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