ﻻ يوجد ملخص باللغة العربية
Non-proportional hazards (NPH) have been observed recently in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitably chosen weights can be used to improve the power of detecting the difference of the two survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice when both robustness and efficiency are considered. A versatile maxcombo test was proposed to achieve the balance of robustness and efficiency and has received increasing attentions in both methodology development and application. However, survival trials often warrant interim analyses due to its high cost and long duration. The integration and application of maxcombo tests in interim analyses often require extensive simulation studies. In this paper, we propose a simulation-free approach for group sequential design with maxcombo test in survival trials. The simulation results support that the proposed approaches successfully control both the type I error rate and offer great accuracy and flexibility in estimating sample sizes, at the expense of light computation burden. Notably, our methods display a strong robustness towards various model misspecifications, and have been implemented in an R package for free access online.
Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple two-stage e
In this manuscript we demonstrate the analysis of right-censored survival outcomes using the MRH package in R. The MRH package implements the multi-resolution hazard (MRH) model, which is a Polya-tree based, Bayesian semi-parametric method for flexib
Simulation-based optimal design techniques are a convenient tool for solving a particular class of optimal design problems. The goal is to find the optimal configuration of factor settings with respect to an expected utility criterion. This criterion
Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space of interest providing a direct link between data and estimator. This
Bayesian inference without the access of likelihood, or likelihood-free inference, has been a key research topic in simulations, to yield a more realistic generation result. Recent likelihood-free inference updates an approximate posterior sequential