Dose-escalation strategies which use subgroup information.

Dose-escalation trials commonly assume a homogeneous trial population to identify a single recommended dose of the experimental treatment for use in future trials. Wrongly assuming a homogeneous population can lead to a diluted treatment effect. Equally, exclusion of a subgroup that could in fact benefit from the treatment can cause a beneficial treatment effect to be missed. Accounting for a potential subgroup effect (ie, difference in reaction to the treatment between subgroups) in dose-escalation can increase the chance of finding the treatment to be efficacious in a larger patient population. A standard Bayesian model-based method of dose-escalation is extended to account for a subgroup effect by including covariates for subgroup membership in the dose-toxicity model. A stratified design performs well but uses available data inefficiently and makes no inferences concerning presence of a subgroup effect. A hypothesis test could potentially rectify this problem but the small sample sizes result in a low-powered test. As an alternative, the use of spike and slab priors for variable selection is proposed. This method continually assesses the presence of a subgroup effect, enabling efficient use of the available trial data throughout escalation and in identifying the recommended dose(s). A simulation study, based on real trial data, was conducted and this design was found to be both promising and feasible.

Bayesian methods for setting sample sizes and choosing allocation ratios in phase II clinical trials with time-to-event endpoints.

Conventional phase II trials using binary endpoints as early indicators of a time-to-event outcome are not always feasible. Uveal melanoma has no reliable intermediate marker of efficacy. In pancreatic cancer and viral clearance, the time to the event of interest is short, making an early indicator unnecessary. In the latter application, Weibull models have been used to analyse corresponding time-to-event data. Bayesian sample size calculations are presented for single-arm and randomised phase II trials assuming proportional hazards models for time-to-event endpoints. Special consideration is given to the case where survival times follow the Weibull distribution. The proposed methods are demonstrated through an illustrative trial based on uveal melanoma patient data. A procedure for prior specification based on knowledge or predictions of survival patterns is described. This enables investigation into the choice of allocation ratio in the randomised setting to assess whether a control arm is indeed required. The Bayesian framework enables sample sizes consistent with those used in practice to be obtained. When a confirmatory phase III trial will follow if suitable evidence of efficacy is identified, Bayesian approaches are less controversial than for definitive trials. In the randomised setting, a compromise for obtaining feasible sample sizes is a loss in certainty in the specified hypotheses: the Bayesian counterpart of power. However, this approach may still be preferable to running a single-arm trial where no data is collected on the control treatment. This dilemma is present in most phase II trials, where resources are not sufficient to conduct a definitive trial.

A practical design for a dual-agent dose-escalation trial that incorporates pharmacokinetic data.

Traditionally, model-based dose-escalation trial designs recommend a dose for escalation based on an assumed dose-toxicity relationship. Pharmacokinetic data are often available but are currently only utilised by clinical teams in a subjective manner to aid decision making if the dose-toxicity model recommendation is felt to be too high. Formal incorporation of pharmacokinetic data in dose-escalation could therefore make the decision process more efficient and lead to an increase in the precision of the resulting recommended dose, as well as decreasing the subjectivity of its use. Such an approach is investigated in the dual-agent setting using a Bayesian design, where historical single-agent data are available to advise the use of pharmacokinetic data in the dual-agent setting. The dose-toxicity and dose-exposure relationships are modelled independently and the outputs combined in the escalation rules. Implementation of stopping rules highlight the practicality of the design. This is demonstrated through an example which is evaluated using simulation.

Modeling to Predict Factor VIII Levels Associated with Zero Bleeds in Patients with Severe Hemophilia A Initiated on Tertiary Prophylaxis.

BACKGROUND: Factor VIII (FVIII) trough levels > 1 IU/dL in patients with severe hemophilia A receiving regular prophylaxis may optimize bleed protection. OBJECTIVES: In this post hoc analysis of patients receiving tertiary prophylaxis for approximately 1 year, the relationship between estimated FVIII levels and reported bleeds was investigated to predict the potential for zero bleeds. METHODS: Sixty-three patients (median [range] age, 28 [7-59] years) with severe hemophilia A (229 bleeds) were included. FVIII levels at time of each bleed were estimated from single-dose individual pharmacokinetics. The highest estimated FVIII level at which patients experienced a bleed was considered the "potentially effective trough level" for that bleed type. Kaplan-Meier estimates of proportions of patients with no bleeds above certain estimated FVIII levels were determined. Those not experiencing a bleed in the trial were assumed to have a bleed at 0 IU/dL (pragmatic approach) or at their median trough level (conservative approach). RESULTS: Kaplan-Meier estimates based on pragmatic approach predicted zero all bleeds, joint bleeds, and spontaneous joint bleeds in 1 year in 40, 43, and 63% of patients, respectively, when the potentially effective trough FVIII level was set at 1 IU/dL. Between 1 and 10 IU/dL, every 1 IU/dL rise in estimated FVIII level was associated with an additional 2% of patients having zero all bleeds. CONCLUSION: This post hoc analysis confirms benefits with trough levels of approximately 1 to 3 IU/dL in most patients starting tertiary prophylaxis; prophylaxis with higher trough levels may help patients to achieve zero bleeds.