Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Re-parameterization of the dose-toxicity model of the time-to-event continual reassessment method to accommodate patient heterogeneity

Sun, Xinxin (2018) Re-parameterization of the dose-toxicity model of the time-to-event continual reassessment method to accommodate patient heterogeneity. Master's Thesis, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Submitted Version

Download (3MB) | Preview

Abstract

The objective of this thesis is to develop a new method, re-parameterizing the logistic dose-toxicity model of the time-to-event continual reassessment method (TITE-CRM) for phase 1 dose-escalation cancer trials, to account for patient heterogeneity.
In a phase 1 dose-escalation clinical trial, the goal is to identify a dose (the maximum tolerated dose or MTD) that is associated with a pre-specified probability of unacceptable toxicity. TITE-CRM is a Bayesian, model-based dose-escalation trial design specificially intended for situations where participants must be observed for toxicity a long period time compared to the rate at which patients present for treatment.
The risk of toxicity may vary due to particpants’ inherent characteristics. For example, patients with lung cancer and concomitant renal disease are not able to tolerate the same dose level of the cancer treatment as the patients without concomitant renal disease. If patient heterogeneity exists, the one-group study design will ignore the patient heterogeneity and give an average MTD, which could result in excess toxicity in the higher risk patients and suboptimal dosing in the lower risk patients.
We have developed a new method to address this issue by re-parameterizing the logisitic model of the TITE-CRM and adding dose-escalation rules to reflect risk group ordering information and control aggressive dose escalation. We assessed the operating characteristics of this design in simulations assuming three risk groups, although the model is trivially extensible to a greater number. We compared this method to parallel trials that use independent one-parameter logistic models. We investigated scenarios with equal numbers of patients in the risk groups and more patients in the higher toxicity risk group, three different true dose-toxicity models and three sample sizes, for a total of 5,400 trials.
According to the results, the TITE-CRM with re-parameterized logistic model used in 3-group trials performs similarly to the TITE-CRM with one-parameter logistic model used in the parallel trials in terms of operating characteristics. However, the TITE-CRM with the re-parameterized logistic model worked better in terms of in-trial dose allocation of patients and recommending the correct final dose in the scenario where there were more patients with higher toxicity risk in a trial. However, estimations of the group risk difference were not precise and the coverages of credible intervals of the group difference parameters were excessively conservative. The method shows promise, and could be implemented now, but further improvements are required. Cancer is one of the major public health concers in the modern society and the new method can be used in the phase 1 dose-escalation cancer trial and have positive impact on the public health.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sun, Xinxinxis54@pitt.eduxis54
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairNormolle, Danieldpn7@pitt.edu
Committee MemberYouk, Adaayouk@pitt.edu
Committee MemberBrooks, Maria MoriMBROOKS@pitt.edu
Date: 20 September 2018
Date Type: Publication
Defense Date: 13 July 2018
Approval Date: 20 September 2018
Submission Date: 4 June 2018
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 104
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: None
Date Deposited: 20 Sep 2018 21:24
Last Modified: 01 Sep 2019 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/34595

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item