Bayesian model averaging of longitudinal dose-response models.

Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation.

Adaptive Designs: Results of 2016 Survey on Perception and Use.

BACKGROUND: The DIA Adaptive Designs Scientific Working Group has a devoted subteam to performing surveys, literature reviews, and registry reviews every 4 years to assess the perception and use of adaptive designs (ADs) in the development of drugs and biologics. METHODS: A survey was distributed to pharmaceutical companies, academic institutions, and contract research organizations to collect information about the usage of ADs of different types and perception of challenges for their use. Literature and registry reviews were conducted to assess the prevalence of ADs of different types in drug and biologics development. These results were compared to previous surveys and reviews using summary statistics. RESULTS: ADs appear to be more widely considered in the last 4 years as compared to earlier 4-year periods. CONCLUSIONS: The most common types of ADs remain early stopping, treatment group adaptations, and sample size re-estimation. Both stopping early for safety and changing the endpoint of the analyses were rarely mentioned in literature prior to 2012 but are now appearing more frequently. The barriers of change management and negative experiences by some institutions with ADs remain a source of concern. Additional, consistent training would be helpful to choose the right adaptation(s) needed for specific clinical trials and for planning appropriately for operational efficiency such as for drug supply management and data management. The perceived barrier of regulatory acceptance also remains a concern, which could be alleviated by additional interaction with agencies and an update of the FDA draft guidance to industry on adaptive designs.

Adaptive designs for comparative effectiveness research trials.

CONTEXT: Medical and health policy decision makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a "minimum clinically meaningful difference". OBJECTIVE: To explore the use of a particular form of ADs for comparing treatments within the CER trial context. METHODS: We review the current state of clinical CER, identify areas of CER as particularly strong candidates for application of novel ADs, and illustrate potential usefulness of the designs and methods for two group comparisons. RESULTS: ADs can stabilize power. The designs ensure adequate power for true effects are at least at clinically significant preplanned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned. CONCLUSION: ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons.

Adaptive trial designs: a review of barriers and opportunities.

Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.