Carl-Fredrik Burman

Impact of Helicobacter pylori eradication on heartburn in patients with gastric or duodenal ulcer disease -- results from a randomized trial programme.

Background : Helicobacter pylori infection has been proposed as a protective factor against the development of gastro‐oesophageal reflux disease.

The HOMER Study: The Effect of Increasing the Dose of Metronidazole When Given with Omeprazole and Amoxicillin to Cure Helicobacter pylori Infection

Background. Helicobacter pylori eradication with omeprazole, amoxycillin, and metronidazole is both effective and inexpensive. However, eradication rates with different dosages and dosing vary, and data on the impact of resistance are sparse. In this study, three different dosages of omeprazole, amoxycillin, and metronidazole were compared, and the influence of metronidazole resistance on eradication was assessed.

Adaptive Designs in Clinical Drug Development: Opportunities, Challenges, and Scope Reflections Following PhRMA's November 2006 Workshop

This paper provides reflections on the opportunities, scope and challenges of adaptive design as discussed at PhRMAs workshop held in November 2006. We also provide a status report of workstreams within PhRMAs working group on adaptive designs, which were triggered by the November workshop. Rather than providing a comprehensive review of the presentations given, we limit ourselves to a selection of key statements. The authors reflect the position of PhRMAs working group on adaptive designs.

Optimizing Drug Development Programs Type 2 Diabetes Case Study

Recently, consideration was given to the impact of dose selection strategies in phase IIb on the overall success of drug development programs. A natural next step is to simultaneously optimize design aspects of both phase IIB and phase III. We used type 2 diabetes as an example, including realistic regulatory and commercial scenarios for this indication. The expected net present value (eNPV) has been selected as the primary outcome because it naturally accommodates optimization, providing an explicit trade-off between the probability of success (PoS) and time delays and trial costs. Our findings are that larger studies and/or implementation of an adaptive design over a fixed design in phase IIb provide more precise dose selection and reduce the bias of treatment effects and uncertainty in the estimated eNPV within the range of sample sizes that we examined. Developers also have to ensure that dose selection criteria are consistent with development strategy and objectives.

Modelling and simulation in the pharmaceutical industry—some reflections

Modelling and simulation (M&S) is increasingly being applied in (clinical) drug development. It provides an opportune area for the community of pharmaceutical statisticians to pursue. In this article, we highlight useful principles behind the application of M&S. We claim that M&S should be focussed on decisions, tailored to its purpose and based in applied sciences, not relying entirely on data-driven statistical analysis. Further, M&S should be a continuous process making use of diverse information sources and applying Bayesian and frequentist methodology, as appropriate. In addition to forming a basis for analysing decision options, M&S provides a framework that can facilitate communication between stakeholders. Besides the discussion on modelling philosophy, we also describe how standard simulation practice can be ineffective and how simulation efficiency can often be greatly improved.

Optimizing Trial Designs for Targeted Therapies

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor’s as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

Late-stage pharmaceutical R&D and pricing policies under two-stage regulation

We present a model combining the two regulatory stages relevant to the approval of a new health technology: the authorisation of its commercialisation and the insurers decision about whether to reimburse its cost. We show that the degree of uncertainty concerning the true value of the insurers maximum willingness to pay for a unit increase in effectiveness has a non-monotonic impact on the optimal price of the innovation, the firms expected profit and the optimal sample size of the clinical trial. A key result is that there exists a range of values of the uncertainty parameter over which a reduction in uncertainty benefits the firm, the insurer and patients. We consider how different policy parameters may be used as incentive mechanisms, and the incentives to invest in R&D for marginal projects such as those targeting rare diseases. The model is calibrated using data on a new treatment for cystic fibrosis.

On the choice of doses for phase III clinical trials

Many potential new medicines fail in phase III clinical trials, because of either insufficient efficacy or intolerability. Such failures may be caused by the absence of an effect and also if a suboptimal dose is being tested. It is thus important to consider how to optimise the choice of dose or doses that continue into the confirmatory phase. For many indications, it is common to test one single active dose in phase III. However, phase IIB dose-finding trials are relatively small and often lack the ability of precisely estimating the dose-response curves for efficacy and tolerability. Because of this uncertainty in dose response, it is reasonable to consider bringing more than one dose into phase III. Using simple but illustrative models, we find the optimal doses and compare the probability of success, for fixed total sample sizes, when one or two active doses are included in phase III.

Optimized adaptive enrichment designs

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.

A decision theoretic approach to optimization of multiple testing procedures

This paper focuses on the concept of optimizing a multiple testing procedure (MTP) with respect to a predefined utility function. The class of Bonferroni-based closed testing procedures, which includes, for example, (weighted) Holm, fallback, gatekeeping, and recycling/graphical procedures, is used in this context. Numerical algorithms for calculating expected utility for some MTPs in this class are given. The obtained optimal procedures, as well as the gain resulting from performing an optimization are then examined in a few, but informative, examples.