Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Bioinformatics and Biostatistics

Committee Chair

Rai, Shesh Nath

Author's Keywords

Clinical trial; Bivariate; Phase II; Phase III


Clinical trials--Methodology; Clinical trials--Statistical methods


The phase II clinical trial is a critical step in the drug development process. In the oncology setting, phase II studies typically evaluate one primary endpoint, which is efficacy. In practice, a binary measurement representing the response to the new treatment defines the efficacy. The single-arm, multiple-stage designs are popular and the Simon 2-Stage design is preferred. Although the study designs evaluate the efficacy, the subject's safety is an important concern. Safety is monitored through the number of grade 3 or grade 4 toxic events. The phase II clinical trial design based on the primary endpoint is typically augmented with an ad hoc monitoring rule. The studies are designed in two steps. First, the sample size and critical values are determined based on the primary endpoint. Then an ad hoc toxicity monitoring rule is applied to the study. Previous authors recommended a method to monitor toxic events after each patient is enrolled which is also known as continuous toxicity monitoring. A trial designed at the JG Brown Cancer Center combined the Simon 2-Stage design with continuous toxicity monitoring. We describe how to integrate the continuous toxicity monitoring methodology with the Simon 2-Stage design for response. Theoretical justification is given for the nominal size, power, probability of early termination (PET), and average sample size (ASN) of the combined testing procedure. A series of simulations were conducted to investigate the performance of the combined procedure. We discover that the type I error rate, type II error rate, PET, and ASN are subject to the correlation between toxicity and response. In fact, the study may have a smaller type I error rate than expected. The theoretical expressions derived to describe the operating characteristics of the combined procedure were utilized to create a new flexible, bivariate, multistage clinical trial. The design is considered flexible because it can monitor toxicity on a different schedule than response. An example is considered in which toxicity is measured after four equally spaced intervals and the response is evaluated only at the second and fourth toxicity examinations. This example corresponds to a data monitoring committee's meeting schedule that may happen every 6 months over a two year span. The effect of the correlation on the type I and type II error rates is examined through simulation. The simulations also examine the power over the range of response rates with a fixed toxicity rate in the alternative region and vice-versa. There are several single-arm, multiple-stage clinical trial designs that consider multiple endpoints at the same time. A subset of the designs includes those that consider both efficacy and toxicity as binary endpoints. A common problem, considered after the conduct of the trial, is appropriate inference given the repeated examinations of the multiple endpoints. We propose a uniformly minimum variance unbiased estimator (UMVUE) for the response in a multistage clinical trial design incorporating toxicity effects. The proposed estimator and the typical maximum likelihood estimator (MLE) are evaluated through simulation. The estimator requires further modification when continuous toxicity monitoring is combined with a multistage design for response. The modified estimator maintains low bias over the range of possible response values. The larger phase lIb or phase III clinical trial is the logical extension of the bivariate research based on exact calculations. The phase lIb or III clinical trials typically include an ad hoc toxicity monitoring rule ensuring participant protection. The designs also include provisions to allow early stopping for futility or efficacy utilizing group sequential theory or stochastic curtailment. We also examine a novel large sample clinical trial design that incorporates correlation between the response and toxicity events. The design uses the typical critical values associated with the standard normal distribution. It also searches for critical values specific to the global hypothesis associated with both response and toxicity. The bivariate test is then combined with efficacy and safety monitoring based on a flexible time-varying conditional power methodology. The type I and type II error rates of the bivariate test procedure, along with the bivariate test procedure combined with the conditional power methodology, are investigated through simulation. A modification is developed for the conditional power methodology to preserve the type I and type II error rates. In the end, the research extends the bivariate clinical trial designs in an attempt to make them more appealing in practice. Although, the research resulted in positive outcomes, additional work is required.