arXiv subjects

Shuji Ando

Publications and source records attributed to Shuji Ando.

3 recordsLinked to original sources

arXiv2025

On window mean survival time with interval-censored data

In recent years, cancer clinical trials have increasingly encountered non proportional hazards (NPH) scenarios, particularly with the emergence of immunotherapy. In randomized controlled trials comparing immunotherapy with conventional chemotherapy or placebo, late difference and early crossing survivals scenarios are commonly observed. In such cases, window mean survival time (WMST), the area under the survival curve within a pre-specified interval $[\tau_0, \tau_1]$, has gained increasing attention due to its superior power compared to restricted mean survival time (RMST), the area under the survival curve up to a pre-specified time point. Considering the increasing use of progression-free survival as a co-primary endpoint alongside overall survival, there is a critical need to establish a WMST estimation method for interval-censored data; however, sufficient research has yet to be conducted. To bridge this gap, this study proposes a WMST inference method utilizing one-point imputations and Turnbull's method. Extensive numerical simulations demonstrate that the WMST estimation method using mid-point imputation for interval-censored data exhibits comparable performance to that using Turnbull's method. Since the former facilitates standard error calculation, we adopt it as the standard method. Numerical simulations on two-sample tests confirm that the proposed WMST testing method have higher power than RMST in late difference and early crossing survival scenarios, while having compatible power to the log-rank test under the PH. Furthermore, even when pre-specified $\tau_0$ deviated from the clinically desirable time point, WMST consistently maintains higher power than RMST in late difference and early crossing survivals scenarios.

stat.AP↗

arXiv2025

Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials through Bayesian Dose-Response Model

Model-assisted designs have garnered significant attention in recent years due to their high accuracy in identifying the maximum tolerated dose (MTD) and their operational simplicity. To identify the MTD, they employ estimated dose limiting toxicity (DLT) probabilities via isotonic regression with pool-adjacent violators algorithm (PAVA) after trials have been completed. PAVA adjusts independently estimated DLT probabilities with the Bayesian binomial model at each dose level using posterior variances ensure the monotonicity that toxicity increases with dose. However, in small sample settings such as Phase I oncology trials, this approach can lead to unstable DLT probability estimates and reduce MTD selection accuracy. To address this problem, we propose a novel MTD identification strategy in model-assisted designs that leverages a Bayesian dose-response model. Employing the dose-response model allows for stable estimation of the DLT probabilities under the monotonicity by borrowing information across dose levels, leading to an improvement in MTD identification accuracy. We discuss the specification of prior distributions that can incorporate information from similar trials or the absence of such information. We examine dose-response models employing logit, log-log, and complementary log-log link functions to assess the impact of link function differences on the accuracy of MTD selection. Through extensive simulations, we demonstrate that the proposed approach improves MTD selection accuracy by more than 10\% in some scenarios and by approximately 6\% on average compared to conventional approach. These findings indicate that the proposed approach can contribute to further enhancing the efficiency of Phase I oncology trials.

stat.AP↗

arXiv2024

A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso (OAL) can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using the OAL was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the OAL to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the OAL for PS models and various outcome-oriented covariate selection via penalization for outcome models. We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. The performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was high. In a clinical trial dataset analysis, the AIPW estimators using outcome-oriented covariate selection via penalization with and without the oracle property showed similar estimates and standard errors.

stat.ME↗