# Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods > **NIH NIH R03** · UNIVERSITY OF FLORIDA · 2021 · $76,250 ## Abstract Project Summary/Abstract Current phase II clinical trial designs for cancer studies are generally not flexible and effective enough to reduce sample size and costs. Unlike traditional single-arm two-stage designs, adaptive designs allow a study to be modified with the information observed from previous stages. Recently, a few adaptive designs were developed for phase II cancer clinical trials with binary endpoints, and the majority of them cannot be directly applied in practice because of a counter-intuitive feature of the relationship between sample size and the number of responses from previous stages. We developed a new single-arm two-stage design that corrects that counter- intuitive feature of the study design. These adaptive designs were all developed for single-arm studies. In Aim 1, we will use efficient integer algorithms along with exact Monte Carlo simulation methods to develop adaptive randomized two-arm designs for cancer clinical trials. The proposed adaptive randomized designs are expected to save between 10% to 35% sample sizes as compared to the conventional group sequential designs. Unlike the existing adaptive randomized designs minimizing expected treatment failures, we will develop the first adaptive randomized designs with the objective to minimize expected sample size. For the existing adaptive single-arm design using integer algorithms without importance sampling, it could take a few months by using a stand-alone computer, and a few days using a supercomputer. With multiple arms in a study, it would be very computationally intensive. The goal of Aim 3 is to reduce the computation time to no more than 30 minutes by utilizing importance sampling and integer algorithms on a stand-alone computer. The traditionally used importance sampling does not guarantee the type I error rate and power. For this reason, we will utilize the recently developed exact importance sampling method to guarantee type I error rate and power. A combination of integer algorithms and importance sampling will be able to reduce the computation time to no more than 30 minutes for the proposed adaptive designs. In addition to new adaptive design development, we will also develop proper statistical inference for adaptive two-stage clinical trials in Aim 2. The existing exact approaches from commercial software for statistical inference are often based on the conditional framework, by assuming both marginal totals fixed. Such exact conditional approaches are not aligned with the study design for a clinical trial which often only assumes the sample size of each arm fixed, not the total responses. The proposed exact statistical inferences are proper by considering the nature of adaptive designs with multiple stages and sample size change. Ultimately, we will develop adaptive randomized designs for phase II cancer studies with binary endpoints with the smallest expected sample size. The proposed designs will be available for public use through a new R package and a ne... ## Key facts - **NIH application ID:** 10405326 - **Project number:** 7R03CA248006-02 - **Recipient organization:** UNIVERSITY OF FLORIDA - **Principal Investigator:** Guogen Shan - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $76,250 - **Award type:** 7 - **Project period:** 2021-05-14 → 2023-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10405326 ## Citation > US National Institutes of Health, RePORTER application 10405326, Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods (7R03CA248006-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10405326. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*