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Convergence fragility in probit Bayesian kernel machine regression implemented in the bkmr R package for binary-outcome environmental mixture analyses: a simulation study
Akifumi Eguchi, Takayuki Kawashima, Tomotaka Momozaki +1
Background. Bayesian kernel machine regression (BKMR) is widely used for exposure-mixture analyses with binary outcomes through a probit extension. Because a bkmr fit can complete…
On window mean survival time with interval-censored data
Takuto Iijima, Tomotaka Momozaki, Shuji Ando
In recent years, cancer clinical trials have increasingly encountered non proportional hazards (NPH) scenarios, particularly with the emergence of immunotherapy. In randomized cont…
Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials through Bayesian Dose-Response Model
Rentaro Wakayama, Tomotaka Momozaki, Shuji Ando
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 simplici…