Novel statistical designs for phase I clinical trials
| dc.contributor.author | Zhang, Weijia | |
| dc.contributor.examiningcommittee | Mandal, Saumen (Statistics) Zheng, Steven (Management) Deng, Dianliang (Mathematics and Statistics, University of Regina) | en_US |
| dc.contributor.supervisor | Yang, Po (Statistics) Muthukumarana, Saman (Statistics) | en_US |
| dc.date.accessioned | 2019-09-05T21:12:35Z | |
| dc.date.available | 2019-09-05T21:12:35Z | |
| dc.date.issued | 2019-08-26 | en_US |
| dc.date.submitted | 2019-08-26T21:31:00Z | en |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | A clinical trial is an experiment on human subjects designed to evaluate the safety and efficacy of a new drug or medical intervention. There are four phases of a clinical trial. Phase I trial is the first step to determine the maximum tolerated dose (MTD) to be used in the subsequent trials. My Ph.D. research is focused on proposing and evaluating statistical designs of Phase I clinical trial. The commonly used parametric design is the continual reassessment method (CRM). This method assumes a parametric statistical model with unknown parameters to describe toxicity probability at each dose level. These unknown parameters follow prior distributions under the Bayesian approach. Patient outcomes are either toxic or nontoxic and these outcomes are used to update posterior mean toxicity probabilities. The objective of a Phase I trial is to determine the MTD, which is the dose whose posterior mean toxicity probability is closest to the target toxicity probability, say 33%, after all patients in the trial are treated. Three classic parametric models are normally used with the CRM, namely the power, logistic and hyperbolic tangent models. In my thesis, we introduce a new class of parametric functions, based on the cumulative distribution function of the normal distribution. A major advantage is that we can choose different values of the mean and variance of the normal distribution to change the location and shape of the dose toxicity probability curve. So our new model is more flexibly. We conduct simulation studies and compare our new design with existing designs, for one drug or the combination of two drugs. We investigate the performance of our new design when we assume that the variance is unknown, and the performance of the Bayesian model averaging CRM design. Finally we derive asymptotic statistical inference of the unknown parameter. We introduce some new performance criteria and compare different models based on “BEARS”: Benchmark, Efficiency, Accuracy, Reliability, Safety. | en_US |
| dc.description.note | October 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34170 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Bayesian method, CRM, Maximum tolerated dose, Phase I clinical trials, Toxicity probability | en_US |
| dc.title | Novel statistical designs for phase I clinical trials | en_US |
| dc.type | doctoral thesis | en_US |