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Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods

Award Number: R03CA248006
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/14/2021
PERIOD OF PERFORMANCE END DATE: 01/31/2024

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Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods - 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 new website that will use a powerful supercomputer. Upon completion of this project, our school will take over the cost of maintenance of the software developed from this proposal.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = -$495 )
2024	2022	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cancer Treatment Research	000	3	5/29/2024	NON-COMPETING CONTINUATION	-$495
														Subtotal = -$495

Issue Date FY: 2022 ( Subtotal = $76,250 )
2022	2022	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cancer Treatment Research	000	3	2/17/2022	NON-COMPETING CONTINUATION	$76,250
														Subtotal = $76,250

Issue Date FY: 2021 ( Subtotal = $76,250 )
2021	2021	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Cancer Treatment Research	002	2	6/5/2021	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$76,250
2021	2021	BOARD OF REGENTS NEVADA SYSTEM OF HIGHER EDUCATION	4505 S MARYLAND PKWY	LAS VEGAS	NV	89154	CLARK	USA	Cancer Treatment Research	000	1	1/15/2021	NEW	$74,750
2021	2021	BOARD OF REGENTS NEVADA SYSTEM OF HIGHER EDUCATION	4505 S MARYLAND PKWY	LAS VEGAS	NV	89154	CLARK	USA	Cancer Treatment Research	001	1	6/4/2021	NEW	-$74,750
														Subtotal = $76,250

Grand Total All Awards = $152,005

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Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods

Award Number: R03CA248006

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/14/2021

PERIOD OF PERFORMANCE END DATE: 01/31/2024

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