Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Bcr: A conditional inhibitor of Bcr -Abl oncoprotein

Chronic myelogenous leukemia (CML) is characterized cytogenetically by the presence of the Philadelphia chromosome and clinically by the clonal expansion of the hematopoietic stem cells and the accumulation of large numbers of myeloid cells. Philadelphia chromosome results from the reciprocal translocation between chromosomes 9 and 22 [t(9;22)(324;q11)], which fuses parts of the ABL proto-oncogene to 5′ portions of the BCR gene. The product of the fused gene is Bcr-Abl oncoprotein. Bcr-Abl oncoprotein has elevated protein tyrosine kinase activity, and is the cause of Philadelphia chromosome associated leukemias. The Bcr sequence in the fusion protein is crucial for the activation of Abl kinase activity and transforming phenotype of Bcr-Abl oncoprotein. Although the Bcr-Abl oncoprotein has been studied extensively, its normal counterpart, the Bcr protein, has been less studied and its function is not well understood. At this point, Bcr is known to encode a novel serine/threonine protein kinase. In Bcr-Abl positive leukemia cells, we found that the serine kinase activity of Bcr is impaired by tyrosine phosphorylation. Both the Bcr protein sequences within Bcr-Abl and the normal cellular Bcr protein lack serine/threonine kinase activity when they become phosphorylated on tyrosine residues by Bcr-Abl. Therefore, the goal of this study was to investigate the role of Bcr in Bcr-Abl positive leukemia cells. We found that overexpression of Bcr can inhibit Bcr-Abl tyrosine kinase activity, and the inhibition is dependent on its intact serine/threonine kinase function. Using the tet repressible promoter system, we demonstrated that Bcr when induced in Bcr-Abl positive leukemia cells inhibited the Bcr-Abl oncoprotein tyrosine kinase. Furthermore, induction of Bcr also increased the number of cells undergoing apoptosis and inhibited the transforming ability of Bcr-Abl. In contrast to the wild-type Bcr, the kinase-inactive mutant of Bcr (Y328F/Y360F) had no effects on Bcr-Abl tyrosine kinase in cells. Results from other experiments indicated that phosphoserine-containing Bcr sequences within the first exon, which are known to bind to the Abl SH2 domain, are responsible for observed inhibition of the Bcr-Abl tyrosine kinase. Several lines of evidence suggest that the phosphoserine form of Bcr, which binds to the Abl SH2 domain, strongly inhibits the Abl tyrosine kinase domain of Bcr-Abl Previously published findings from our laboratory have also shown that Bcr is phosphorylated on tyrosine residue 177 in Bcr-Abl positive cells and that this form of Bcr recruits the Grb2 adaptor protein, which is known to activate the Ras pathway. These findings implicate Bcr as an effector of Bcr-Abl's oncogenic activity. Therefore based on the findings presented above, we propose a model for dual Function of Bcr in Bcr-Abl positive leukemia cells. Bcr, when active as a serine/threonine kinase and thus autophosphorylating its own serine residues, inhibits Bcr-Abl's oncogenic functions. However, when Ber is tyrosine phosphorylated, its Bcr-Abl inhibitory function is neutralized thus allowing Bcr-Abl to exert its full oncogenic potential. Moreover, tyrosine phosphorylated Bcr would compliment Bcr-Abl's neoplastic effects by the activation of the Ras signaling pathway. ^