Haitao Pan Group

In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of the continuous response. This UnifiedDoseFinding package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess the design's operating characteristics under various scenarios. Three dose-finding designs are included in this package: unified phase I design (Ivanova et al. (2009)), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007), Pan et al. (2014)) and generalized BOIN design (Mu et al. (2018)). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into the dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules. Download the software package here and an accompanying paper here.

Download the R Shiny App for MinEDfind here.

An R package Keyboard containing functions for the implementation and simulation of two phase I model-assisted maximum tolerated dose (MTD)-finding designs for single-agent and combination trials, and one biological dose (OBD)-finding phase I/II design. Download the software package here.

Download the R Shiny app for conducting Phase I dose-finding trials with dose-insertion method here.

An R package MinEDfind containing functions for the implementation and simulation of a Bayesian nonparametric two-stage design for minimum effective dose-finding can be downloaded here. For Windows, the MinEDfind library can be downloaded from within R GUI by clicking on Packages/Install package(s), choose a mirror site, and locating MinEDfind. Questions regarding the R code, the literature, and the application of MinEDfind are welcome.

A seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy. Download here.

The description text that displays when the accordion expands should read "A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the response rate) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal_2arm_binary() or optimal_3arm_binary() function) for each arm. All the design needed components (sample size, operating characteristics) are supported. Download the software package.

A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though the survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD won't control the type I error rate. The proposed designs can "partially" control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported. Download the software package here.

The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint. Download the software package here.

The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios.  Download the software package here.

Design parameters can be calculated using this package based on the optimal two-period multi-arm platform design allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multi-arm platform design with adding new arms". Manuscript submitted for publication. Download the software package here.

The proposed two-stage design can be used for single-arm phase II trial designs with time-to-event endpoints, which is desirable for clinical trials on immunotherapies among cancer patients. There're two advantages of the proposed approach: 1) It provides flexible choices of four underlying survival distributions and 2) the power of the design is more accurately calculated using exact variance in one-sample log-rank test. The package can be used for 1) planning the sample size; 2) conducting the interim and final analyses for the Go/No-go decisions. Download the software package here.

Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials. The BAR package can help users develop the protocol using this method. You can download the package here.

Methods for managing under- and over-enrollment in Simon's Two-Stage Design are offered by providing adaptive threshold adjustments and sample size recalibration. It also includes post-inference analysis tools to support clinical trial design and evaluation. The package is designed to enhance flexibility and accuracy in trial design, ensuring better outcomes in oncology and other clinical studies. Download the software package.