arXiv subjects

Linda Sun

Publications and source records attributed to Linda Sun.

3 recordsLinked to original sources

arXiv2026

Two-dose vs. Three-Dose Optimization Under Sample Size Constraint

Dose optimization is a hallmark of Project Optimus for oncology drug development. The number of doses to include in a dose optimization study depends on the totality of evidence, which is often unclear in early-phase development. With equal sample sizes per dose, carrying three doses is clearly more advantageous than two for optimization. In this paper, we show that, even when the total sample size is fixed, it is still preferable to carry three unless there is very strong evidence that one can be dropped. A mathematical approximation is applied to guide the investigation, followed by a simulation study to complement the theoretical findings. Semi-quantitative guidance is provided for practitioners, addressing both randomized and non-randomized dose optimization while considering population homogeneity.

stat.ME↗

arXiv2024

Generalized Design of Basket Trials with P-value Combination Test

The oncology exploratory basket trial design with pruning and pooling (P&P) approach has gained increasing popularity in recent years for its simplicity and efficiency. This method was proposed based on binary endpoint, limiting its wider application. This short communication proposed a generalized framework of using P-value combination test to implement pruning and pooling process in basket trials. Only P-values of any type of statistical testing from each cohort are needed for decision making, which provides great flexibility for basket trial designs with P&P approach.

stat.AP↗

arXiv2020

Multiplicity for a Group Sequential Trial with Biomarker Subpopulations

Biomarker subpopulations have become increasingly important for drug development in targeted therapies. The use of biomarkers has the potential to facilitate more effective outcomes by guiding patient selection appropriately, thus enhancing the benefit-risk profile and improving trial power. Studying a broad population simultaneously with a more targeted one allows the trial to determine the population for which a treatment is effective and allows a goal of making approved regulatory labeling as inclusive as is appropriate. We examine new methods accounting for the complete correlation structure in group sequential designs with hypotheses in nested subgroups. The designs provide full control of family-wise Type I error rate. This extension of previous methods accounting for either group sequential design or correlation between subgroups improves efficiency (power or sample size) over a typical Bonferroni approach for testing nested populations.

stat.ME↗