Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

Novel sequential methods in dose-finding designs : extensions of the continual reassessment method

Dose-finding trials are a form of clinical data collection process in which the primary objective is to estimate an optimum dose of an investigational new drug when given to a patient. This thesis develops and explores three novel dose-finding design methodologies. All design methodologies presented in this thesis are pragmatic. They use statistical models, incorporate clinicians' prior knowledge efficiently, and prematurely stop a trial for safety or futility reasons. Designing actual dose-finding trials using these methodologies will minimize practical difficulties, improve efficiency of dose estimation, be flexible to stop early and reduce possible patient discomfort or harm.

Incorporating adverse event relatedness into dose-finding clinical trial designs

Dose-finding designs estimate the dose level of a drug based on observed adverse events. Relatedness of the adverse event to the drug has been generally ignored in all proposed design methodologies. These designs assume that the adverse events observed during a trial are definitely related to the drug, which can lead to flawed dose-level estimation. We incorporate adverse event relatedness into the so-called continual reassessment method. Adverse events that have ‘doubtful’ or ‘possible’ relationships to the drug are modelled using a two-parameter logistic model with an additive probability mass. Adverse events ‘probably’ or ‘definitely’ related to the drug are modelled using a cumulative logistic model. To search for the maximum tolerated dose, we use the maximum estimated toxicity probability of these two adverse event relatedness categories. We conduct a simulation study that illustrates the characteristics of the design under various scenarios. This article demonstrates that adverse event relatedness is important for improved dose estimation. It opens up further research pathways into continual reassessment design methodologies.

A simple Bayesian decision-theoretic design for dose-finding trials

A flexible and simple Bayesian decision-theoretic design for dose-finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose-toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one-step-look-ahead (OSLA), which selects the best-so-far dose. A more complicated rule, such as the two-step-look-ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two-step-look-ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage.

Decision-theoretic designs for dose-finding clinical trials with multiple outcomes

A decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available.

A dose-finding study of lanreotide (a somatostatin analog) in patients with colorectal carcinoma

BACKGROUND. Laboratory data suggest that insulin-like growth factor-1 (IGE-1) may stimulate the growth of different human tumors. At least in acromegalic patients, somatostatin (SMS) analogs, such as lanreotide, suppress the serum levels of growth hormone (GH) and IGE-1. METHODS. To evaluate the tolerability and biologic activity of different doses of lanreotide in patients with advanced colorectal carcinoma, consecutive groups of 3 patients each were subcutaneous treated with lanreotide at doses of 1, 2, 3, 4, 5, or 6 mg three times a day for 2 months. In the event of Grade 3 side effects, 3 additional patients were treated with the same dose before the next dose escalation. Serum samples were obtained on Days 0, 15, 30, and 60 for serum GH, IGF-1, and lanreotide assessment. RESULTS. Twenty-four patients were enrolled and all were evaluable. Except for the 3 and 6 mg doses, for which the observation of a Grade 3 side effect required that an additional three patients be treated, it was sufficient to treat 3 patients at each dose. The overall incidence of side effects was as follows: changes in bowel habits, 83%; abdominal cramps, 79%; diarrhea, 17%; vomiting, 17%; nausea, 21%; steatorrhea, 78%; hyperglycemia, 35%; laboratory hypothyroidism, 39%; gallstones, 13%; and weight loss, 17%. No evidence of an increase in the incidence, intensity, or duration of side effects was observed with dose escalation. Serum IGF-1 levels were as follows: Day 13: 63%, 60%, and 67% of the baseline values for the low (12 mg), intermediate (3-4 mg), and high (5- 6 mg) dose groups, respectively; Day 30: 63%, 59%, and 51%, respectively; and Day 60: 73%, 69%, and 47%, respectively. Serum lanreotide levels declined during treatment in all of the dose groups (90 ng/mL on Day 15, and 35 ng/mL on Day 60 for the 5-6 mg group; 10 ng/mL on Day 15, and 1.5 ng/mL on Day 60 for the 1-2 mg group). No antitumor activity or tumor marker reduction was observed. CONCLUSIONS. No increase in toxicity was observed when subcutaneous lanreotide doses were escalated to 6 mg three times a day for 2 months. The highest doses seemed to maintain reduced serum IGF-1 levels; with the lowest doses, a 'rebound' in serum IGF-1 levels was observed during treatment. Nevertheless, intermittent subcutaneous injections do not ensure constant serum drug concentrations over time.

A Randomized, Double-Blind, Dose-Finding, Multicenter, Phase 2 Study of Radium Chloride (Ra 223) in Patients with Bone Metastases and Castration-Resistant Prostate Cancer

BACKGROUND: Patients with castration-resistant prostate cancer (CRPC) and bone metastases have an unmet clinical need for effective treatments that improve quality of life and survival with a favorable safety profile. OBJECTIVE: To prospectively evaluate the efficacy and safety of three different doses of radium chloride (Ra 223) in patients with CRPC and bone metastases. DESIGN, SETTING, AND PARTICIPANTS: In this phase 2 double-blind multicenter study, 122 patients were randomized to receive three injections of Ra 223 at 6-wk intervals, at doses of 25 kBq/kg (n=41), 50 kBq/kg (n=39), or 80 kBq/kg (n=42). The study compared the proportion of patients in each dose group who had a confirmed decrease of =50% in baseline prostate-specific antigen (PSA) levels. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Efficacy was evaluated using blood samples to measure PSA and other tumor markers, recorded skeletal-related events, and pain assessments. Safety was evaluated using adverse events (AEs), physical examination, and clinical laboratory tests. The Jonckheere-Terpstra test assessed trends between groups. RESULTS AND LIMITATIONS: The study met its primary end point with a statistically significant dose-response relationship in confirmed =50% PSA declines for no patients (0%) in the 25-kBq/kg dose group, two patients (6%) in the 50-kBq/kg dose group, and five patients (13%) in the 80-kBq/kg dose group (p=0.0297). A =50% decrease in bone alkaline phosphatase levels was identified in six patients (16%), 24 patients (67%), and 25 patients (66%) in the 25-, 50-, and 80-kBq/kg dose groups, respectively (p

Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use

Adaptive designs for Phase I dose-finding studies

Improving methodology for Phase I dose-finding studies is currently of great interest in pharmaceutical and medical research. This article discusses the current atmosphere and attitude towards adaptive designs and focuses on the influence of Bayesian approaches.