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Bristol Myers Squibb

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Selected work

Representative Papers

Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams

Jan 23, 2025

This study addresses the challenge of estimating treatment effects under multiple interval-censored data. We propose the first semiparametric modeling framework for multistate semi-Markov models and develop a Monte Carlo EM (MCEM) algorithm based on importance sampling to overcome computational bottlenecks arising from high-dimensional, asynchronous observations under coarsening mechanisms. Applied to the REGEN-COV monoclonal antibody clinical trial evaluating household secondary SARS-CoV-2 transmission prevention, our method integrates heterogeneous interval-censored data—including symptom onset, RT-qPCR viral load trajectories, and serological outcomes—to quantify effects on asymptomatic infection risk, viral shedding duration, and seroconversion rate. Results show that REGEN-COV significantly reduces asymptomatic infection risk (HR = 0.32), shortens median viral shedding by 4.1 days, and suppresses seroconversion among asymptomatic individuals. The proposed algorithm achieves 3–5× computational efficiency gains over existing methods, enabling robust modeling of complex real-world interval-censored data.

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Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials

Sep 28, 2026

This study addresses the high cost and prolonged duration associated with measuring primary outcomes in randomized trials by proposing a blinded adaptive sampling design. The method leverages auxiliary variables to dynamically adjust sampling probabilities while ensuring statisticians remain blinded to treatment assignments. Treatment effects are estimated by combining augmented inverse probability weighting (AIPW) with residual variance modeling, and variance loss upper bounds along with valid confidence intervals are derived based on the martingale central limit theorem. Compared with simple random sampling, the proposed approach improves efficiency by 12%–34% and reduces required sample sizes by 10%–26%. In a reanalysis of an antifungal trial, the method achieved a 39% reduction in variance, substantially enhancing statistical power.

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Information Borrowing for Cox Regression with an Auxiliary Outcome

Sep 27, 2026

This study addresses how to effectively leverage auxiliary outcome information to enhance the estimation efficiency of Cox regression in survival analysis. To this end, it proposes a joint-likelihood-free semiparametric framework built upon the Cox proportional hazards and partial linear models. This approach facilitates cross-source information borrowing by linking baseline covariate effects through a quadratic penalty, while an adaptive penalization mechanism is designed to optimize the bias–variance trade-off. The asymptotic properties of the proposed estimator are rigorously derived. Both simulation studies and real-data applications demonstrate that the method substantially improves estimation efficiency while preserving unbiasedness.

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Recent publications

Latest Papers

Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials

Sep 28, 2026

This study addresses the high cost and prolonged duration associated with measuring primary outcomes in randomized trials by proposing a blinded adaptive sampling design. The method leverages auxiliary variables to dynamically adjust sampling probabilities while ensuring statisticians remain blinded to treatment assignments. Treatment effects are estimated by combining augmented inverse probability weighting (AIPW) with residual variance modeling, and variance loss upper bounds along with valid confidence intervals are derived based on the martingale central limit theorem. Compared with simple random sampling, the proposed approach improves efficiency by 12%–34% and reduces required sample sizes by 10%–26%. In a reanalysis of an antifungal trial, the method achieved a 39% reduction in variance, substantially enhancing statistical power.

0 citationsRead paper

Information Borrowing for Cox Regression with an Auxiliary Outcome

Sep 27, 2026

This study addresses how to effectively leverage auxiliary outcome information to enhance the estimation efficiency of Cox regression in survival analysis. To this end, it proposes a joint-likelihood-free semiparametric framework built upon the Cox proportional hazards and partial linear models. This approach facilitates cross-source information borrowing by linking baseline covariate effects through a quadratic penalty, while an adaptive penalization mechanism is designed to optimize the bias–variance trade-off. The asymptotic properties of the proposed estimator are rigorously derived. Both simulation studies and real-data applications demonstrate that the method substantially improves estimation efficiency while preserving unbiasedness.

0 citationsRead paper

Pattern-Based Sequential Multiple Imputation for Missing Data in Clinical Trials: An Extension for Baseline-Only Early Dropout Subjects

Aug 17, 2026

This study addresses the challenge of imputing missing data for subjects with baseline-only early withdrawal in clinical trials by proposing the EPSMI-Y1 method. By integrating covariate-matched donor imputation for the first post-baseline visit with an extended pattern-mixture sequential multiple imputation framework, this approach overcomes the reliance of traditional sequential imputation on post-baseline observations and aligns effectively with treatment policy estimands. Empirical evaluations demonstrate that under informative early withdrawal mechanisms, EPSMI-Y1 significantly reduces estimation bias and improves confidence interval coverage while maintaining controlled Type I error rates. Consequently, this method provides a robust statistical solution for handling this specific missing data pattern, facilitating more reliable inference in clinical trial analyses where early dropout is non-ignorable.

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