||5R01CA148713-04 Interpret this number
||University Of Michigan
||Statistical Methods and Issues for Implementing Adaptive Phase I Trials
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
A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients.
Zhang J, Braun TM
J Am Stat Assoc, 2013 Jan 1;108(503), p. null.
Using joint utilities of the times to response and toxicity to adaptively optimize schedule-dose regimes.
Thall PF, Nguyen HQ, Braun TM, Qazilbash MH
Biometrics, 2013 Sep;69(3), p. 673-82.
2013 Aug 19.
Adaptive prior variance calibration in the Bayesian continual reassessment method.
Zhang J, Braun TM, Taylor JM
Stat Med, 2013 Jun 15;32(13), p. 2221-34.
2012 Sep 17.