arXiv subjects

Shouhao Zhou

Publications and source records attributed to Shouhao Zhou.

7 recordsLinked to original sources

arXiv2025

WOW: WAIC-Optimized Gating of Mixture Priors for External Data Borrowing

The integration of external data using Bayesian mixture priors has become a powerful approach in clinical trials, offering significant potential to improve trial efficiency. Despite their strengths in analytical tractability and practical flexibility, existing methods such as the robust meta-analytic-predictive (rMAP) and self-adapting mixture (SAM) often presume borrowing without rigorously assessing whether external information is appropriate to incorporate. When external and concurrent data are discordant, excessive borrowing can bias estimation and lead to misleading conclusions. To address this, we introduce WOW, a Kullback-Leibler-based gating strategy guided by the widely applicable information criterion (WAIC). Within the mixture-prior framework, WAIC-Optimized Weighting (WOW) conducts a preliminary compatibility assessment between external and concurrent trial data to determine eligibility for borrowing. Only if this gating criterion is satisfied does borrowing proceed; a downstream mixture prior procedure, using user-specified fixed or adaptive weights, can then be applied to determine the amount of borrowing. Simulation studies demonstrate that incorporating the WOW strategy before Bayesian mixture prior borrowing methods effectively mitigates excessive borrowing and improves estimation accuracy. A real-data illustration further highlights the feasibility and interpretability of the proposed gate-then-borrow strategy. By providing a practical safeguard against inappropriate borrowing, WOW strengthens the reliability of mixture-prior methods and supports better decision-making in clinical trials.

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arXiv2025

Shiny-MAGEC: A Bayesian R Shiny Application for Meta-analysis of Censored Adverse Events

Accurate assessment of adverse event (AE) incidence is critical in clinical cancer research for drug safety evaluation and regulatory approval. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurring below study-specific reporting thresholds are often omitted from publications, leading to left-censored data. Failure to account for these censored AE counts can result in biased AE incidence estimates. We present an R Shiny application that implements a one-stage Bayesian meta-analysis model specifically designed to incorporate censored AE data into the estimation process. This interactive tool provides a user-friendly interface for researchers to conduct AE meta-analyses and estimate the AE incidence probability following the bias-correction methods proposed by Qi et al. (2024). It also enables direct comparisons between models that either incorporate or ignore censoring, highlighting the biases introduced by conventional approaches. This tutorial demonstrates the Shiny application's functionality through an illustrative example on meta-analysis of PD-1/PD-L1 inhibitor safety and highlights the importance of this tool in improving AE risk assessment. Ultimately, the new Shiny app facilitates more accurate and transparent drug safety evaluations. The Shiny-MAGEC app is available at: https://zihanzhou98.shinyapps.io/Shiny-MAGEC/.

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arXiv2024

Interrater agreement statistics under the two-rater dichotomous-response case with correlated decisions

Measurement of the interrater agreement (IRA) is critical in various disciplines. To correct for potential confounding chance agreement in IRA, Cohen's kappa and many other methods have been proposed. However, owing to the varied strategies and assumptions across these methods, there is a lack of practical guidelines on how these methods should be preferred even for the common two-rater dichotomous rating. To fill the gaps in the literature, we systematically review nine IRA methods and propose a generalized framework that can simulate the correlated decision processes behind the two raters to compare those reviewed methods under comprehensive practical scenarios. Based on the new framework, an estimand of "true" chance-corrected IRA is defined by accounting for the "probabilistic certainty" and serves as the comparison benchmark. We carry out extensive simulations to evaluate the performance of the reviewed IRA measures, and an agglomerative hierarchical clustering analysis is conducted to assess the inter-relationships among the included methods and the benchmark metric. Recommendations for selecting appropriate IRA statistics in different practical conditions are provided and the needs for further advancements in IRA estimation methodologies are emphasized.

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arXiv2024

Meta-analysis of Censored Adverse Events

Meta-analysis is a powerful tool for assessing drug safety by combining treatment-related toxicological findings across multiple studies, as clinical trials are typically underpowered for detecting adverse drug effects. However, incomplete reporting of adverse events (AEs) in published clinical studies is a frequent issue, especially if the observed number of AEs is below a pre-specified study-dependent threshold. Ignoring the censored AE information, often found in lower frequency, can significantly bias the estimated incidence rate of AEs. Despite its importance, this common meta-analysis problem has received little statistical or analytic attention in the literature. To address this challenge, we propose a Bayesian approach to accommodating the censored and possibly rare AEs for meta-analysis of safety data. Through simulation studies, we demonstrate that the proposed method can improves accuracy in point and interval estimation of incidence probabilities, particularly in the presence of censored data. Overall, the proposed method provides a practical solution that can facilitate better-informed decisions regarding drug safety.

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arXiv2022

Evidence synthesis with reconstructed survival data

We present a general approach to synthesizing evidence of time-to-event endpoints in meta-analyses of aggregate data (AD). Our work goes beyond most previous meta-analytic research by using reconstructed survival data as a source of information. A Bayesian multilevel regression model, called the "meta-analysis of reconstructed survival data" (MARS), is introduced, by modeling and integrating reconstructed survival information with other types of summary data, to estimate the hazard ratio function and survival probabilities. The method attempts to reduce selection bias, and relaxes the presumption of proportional hazards in individual clinical studies from the conventional approaches restricted to hazard ratio estimates. Theoretically, we establish the asymptotic consistency of MARS, and investigate its relative efficiency with respect to the individual participant data (IPD) meta-analysis. In simulation studies, the MARS demonstrated comparable performance to IPD meta-analysis with minor deviation from the true values, suggesting great robustness and efficiency achievable in AD meta-analysis with finite sample. Finally, we applied MARS in a meta-analysis of acute myeloid leukemia to assess the association of minimal residual disease with survival, to help respond to FDA's emerging concerns on translational use of surrogate biomarker in drug development of hematologic malignancies.

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arXiv2020

A Note on Bayesian Modeling Specification of Censored Data in JAGS

Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() to model censored data misspecifies the computation of deviance function, which may limit its usage to perform likelihood based model comparison. To establish an automatic approach to specify the correct deviance function in JAGS, we propose a simple alternative modeling strategy to implement Bayesian model selection for analysis of censored outcomes. The proposed approach is applicable to a broad spectrum of data types, which include survival data and many other right-, left- and interval-censored Bayesian model structures.

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arXiv2020

Posterior Averaging Information Criterion

We propose a new model selection method, the posterior averaging information criterion, for Bayesian model assessment from a predictive perspective. The theoretical foundation is built on the Kullback-Leibler divergence to quantify the similarity between the proposed candidate model and the underlying true model. From a Bayesian perspective, our method evaluates the candidate models over the entire posterior distribution in terms of predicting a future independent observation. Without assuming that the true distribution is contained in the candidate models, the new criterion is developed by correcting the asymptotic bias of the posterior mean of the log-likelihood against its expected log-likelihood. It can be generally applied even for Bayesian models with degenerate non-informative prior. The simulation in both normal and binomial settings demonstrates decent small sample performance.

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