On Simon's two-stage design for single-arm phase IIA cancer clinical trials under Beta-binomial distribution

The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials

Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.

Application of Bayesian predictive probability for interim futility analysis in single-arm phase II trial

Background

Bayesian predictive probability design, with a binary endpoint, is gaining attention for the phase II trial due to its innovative strategy. To make the Bayesian design more accessible, we elucidate this Bayesian approach with a R package to streamline a statistical plan, so biostatisticians and clinicians can easily integrate the design into clinical trial.

Methods

We utilize a Bayesian framework using Bayesian posterior probability and predictive probability to build a R package and develop a statistical plan for the trial design. With pre-defined sample sizes, the approach employs the posterior probability with a threshold to calculate the minimum number of responders needed at end of the study to claim efficacy. Then the predictive probability is applied to evaluate future success at interim stages and form stopping rule at each stage.

Results

An R package, ‘BayesianPredictiveFutility’, with associated graphical interface is developed for easy utilization of the trial design. The statistical tool generates a professional statistical plan with comprehensive results including a summary, details of study design, a series of tables and figures from stopping boundary for futility, Bayesian predictive probability, performance [probability of early termination (PET), type I error, and power], PET at each interim analysis, sensitivity analysis for predictive probability, posterior probability, sample size, and beta prior distribution. The statistical plan presents the methodology in a readable language fashion while preserving rigorous statistical arguments. The output formats (Word or PDF) are available to communicate with physicians or to be incorporated in the trial protocol. Two clinical trials in lung cancer are used to demonstrate its usefulness.

Conclusions

Bayesian predictive probability method presents a flexible design in clinical trial. The statistical tool brings an added value to broaden the application.

A phase II open-label study of the intravenous administration of homoharringtonine in the treatment of myelodysplastic syndrome

Homoharringtonine is an alkaloid inhibitor of protein synthesis with activity in myeloid malignancies. We report a phase II pilot study of homoharringtonine in myelodysplastic syndrome (MDS). Induction consisted of homoharringtonine at 2.5 mg/m(2) via continuous infusion for 7 days. Maintenance was given every 4 weeks. Nine patients were enrolled: five with refractory anaemia with excess blasts, two with refractory anaemia with excess blasts in transformation, one each with refractory anaemia and chronic myelomonocytic leukaemia respectively. Median age was 70 years (55-84) and 6 (66%) were male. Per International Prognostic Scoring System (IPSS) two patients were intermediate-1, five intermediate-2 and two high-risk. Median chemotherapy courses were one (1-3). One patient (11%) responded with complete haematological and cytogenetic remission after one course. Eight patients did not respond (four had stable disease, two progressed to acute leukaemia and two died during induction - from aspergillus pneumonia and intracerebral haemorrhage respectively). Grade 3/4 myelosuppression seen in 56% (5/9). Serious non-haematological toxicities included one case of grade 4 left bundle branch heart block and one grade 3 nephrotoxicity. Median time between courses was 42 days (35-72 days). In conclusion homoharringtonine might have clinical activity in some patients with MDS.

A type of sample size design in cancer clinical trials for response rate estimation

During the early stage of cancer clinical trials, when it is not convenient to construct an explicit hypothesis testing, a study on a new therapy often calls for a response rate (p) estimation concurrently with or right before a typical phase II study. We consider a two-stage process, where the acquired information from Stage I (with a small sample size (m)) would be utilized for sample size (n) recommendation for Stage II study aiming for a more accurate estimation. Once a sample size design and a parameter estimation protocol are applied, we study the overall utility (cost-effectiveness) in connection with the cost due to patient recruitment and treatment as well as the loss due to mean squared error from parameter estimation. Two approaches will be investigated including the posterior mixture method (a Bayesian approach) and the empirical variance method (a frequentist approach). We also discuss response rate estimation under truncated parameter space using maximum likelihood estimation with regard to sample size and mean squared error. The profiles of p-specific expected sample size, mean squared error and risk under different approaches motivate us to introduce the concept of "admissible sample size (design)".