Indiana University-Purdue University Indianapolis (IUPUI) / The late-onset outcome issue is common in early phase dose- nding clinical trials.
This problem becomes more intractable in phase I/II clinical trials because both toxicity
and e cacy responses are subject to the late-onset outcome issue. The existing
methods applying for the phase I trials cannot be used directly for the phase I/II trial
due to a lack of capability to model the joint toxicity{e cacy distribution. We propose
a conditional weighted likelihood (CWL) method to circumvent this issue. The
key idea of the CWL method is to decompose the joint probability into the product of
marginal and conditional probabilities and then weight each probability based on each
patient's actual follow-up time. We further extend the proposed method to handle
more complex situations where the late-onset outcomes are competing risks or semicompeting
risks outcomes. We treat the late-onset competing risks/semi-competing
risks outcomes as missing data and develop a series of Bayesian data-augmentation
methods to e ciently impute the missing data and draw the posterior samples of
the parameters of interest. We also propose adaptive dose- nding algorithms to allocate
patients and identify the optimal biological dose during the trial. Simulation
studies show that the proposed methods yield desirable operating characteristics and
outperform the existing methods.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/26380 |
| Date | 07 1900 |
| Creators | Zhang, Yifei |
| Contributors | Zhang, Yong, Song, Yiqing, Liu, Hao, Bakoyannis, Giorgos |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0019 seconds