Grant Number:	1R01CA148713-01 Interpret this number
Primary Investigator:	Braun, Thomas
Organization:	University Of Michigan At Ann Arbor
Project Title:	Statistical Methods and Issues for Implementing Adaptive Phase I Trials
Fiscal Year:	2010

DESCRIPTION (provided by applicant): The six aims in this proposal are motivated by important and relevant issues in the design of adaptive Phase I trials where focused research is needed. The investigators in this proposal bring a uniquely strong combination of statistical methodology, applied clinical trial experience, and computer programming skills to impact the future of oncology clinical trials. Successful completion of the proposed research will substantially augment existing Phase I methodology and provide new insight into novel adaptive Phase I trials approaches that will be important to both methodologic and applied statisticians. Most important, our findings will be relevant to the NIH mission of making important discoveries that improve peoples health and save lives. Aim 1 concerns fundamental issues in model construction for the Continual Reassessment Method (CRM) that are ignored in published literature but have a direct impact on the success of the trial. Aims 2, 3, and 4 share an underlying theme of improving the efficiency of Phase I trials by incorporating additional patient information into the dose-finding process. This information relates to both the prior history of the patient as well as the treatment of their cancer during the trial, data that are routinely collected for general clinical purposes that could impart additional information about the toxicity profile of an agent, but that are often ignored in Phase I studies. Aim 2 proposes four modeling approaches to incorporate patient heterogeneity in adaptive designs, Aim 3 investigates two approaches for incorporating non-dose-limiting toxicities into the estimation of the MTD, while Aim 4 examines approaches to incorporate non-monotonic efficacy patterns. Aim 5 proposes methods for inference about the DLT rate for each dose after a Phase I trial has completed enrollment, information that is rarely considered once a trial is completed and the recommended MTD is found, but provides information for the uncertainty surrounding the selected MTD and its neighboring doses. The lack of freely available and modifiable software remains the major barrier to the implementation of adaptive Phase I trial designs into routine clinical practice. Therefore, this proposal contains a final, sixth aim, spanning all four years of the proposal, focused solely on the programming, in both SAS and R, of all methods described in this proposal, as well as existing methods that have yet to be housed in a single software package. Through this final aim, we will provide a suite of software packages that meet the general needs of researchers working on Phase I design methodology and the specific day-to-day needs of those who administer actual Phase I trials, with the eventual goal of making adaptive Phase I trial designs commonplace in oncology trials published in the coming decade. PUBLIC HEALTH RELEVANCE: Phase I trials are a crucial first step in the discovery of new agents, either alone or in combination with existing agents, for the treatment of cancer. Successful drug discovery hinges on superior clinical trial designs, as well as freely available software to implement those designs. This proposal will examine needed improvements to adaptive Phase I trial designs and provide user-friendly software that facilitates the implementation of those improvements into actual clinical research. The findings will be relevant to the NIH mission and provide new insight to those who either design adaptive Phase I trials or administer actual adaptive Phase I trials.




Bayesian estimation of multivariate normal mixtures with covariate-dependent mixing weights, with an application in antimicrobial resistance monitoring.
Authors: Jaspers S. , Komárek A. , Aerts M. .
Source: Biometrical Journal. Biometrische Zeitschrift, 2018 Jan; 60(1), p. 7-19.
EPub date: 2017-09-12 00:00:00.0.
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