What is the value of adaptive designs? Estimating expected value of sample information for adaptive trial designs.

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Aim: To develop expected value of sample information (EVSI) methods that allow efficient comparison of economic value of conventional fixed and adaptive approaches for a trial design.Background: Healthcare decision makers, such as the National Institute for Health and Care Excellence (NICE) in the UK, are guided in their decisions by cost-effectiveness analysis. These are trial- or model-based comparisons of the costs and effects of interventions for diseases. The value of eliminating the uncertainty in these decision recommendations is the expected value of perfect information (EVPI). The value of eliminating only a subset of the uncertainties is the expected value of partial perfect information (EVPPI). Finally, the EVSI is the economic value of reducing, rather than eliminating, uncertainty via a particular trial design.Adaptive designs are those that adapt in response to results as they accumulate. Examples include: multi-arm multi-stage (MAMS) trials that drop or add treatment arms; trials that change randomisation ratios; and population enrichment trials that focus recruitment on patients most likely to benefit, or in whom benefit is most uncertain. Such designs can achieve the same level of accuracy and precision but with fewer patients assigned to non-performing or harmful treatments, smaller overall sample sizes, shorter durations, or lower trial costs. Adaptive designs can also test multiple hypotheses, potentially offering a better return on investment. Adaptive designs have been growing in popularity but remain the exception rather than the rule. EVSI has only been applied to a limited range of adaptive designs, with computational burden being a primary barrier to wider usage. I will extend and develop computationally efficient EVSI methods to adaptive designs to assess their economic value compared with conventional designs.Work stream 1. Extending efficient methods for estimating EVSI to adaptive designs. When cost-effectiveness analysis relies on complex models, such as Markov multi-state models, estimating EVSI for any trial can represent a substantial computational challenge. This is especially true for adaptive designs. Recent work has focussed on methods to approximate the cost-effectiveness model, including regression via flexible Gaussian processes or generalised additive models, approximation via splines or Taylor series, and moment matching of cost and effect distributions. These have been explored for valuing fixed but not adaptive designs. I will extend these methods to valuing the economic benefits of different adaptive designs.Work stream 2. Develop advanced Monte Carlo sampling schemes to estimate the EVSI of adaptive designs.The second approach I will investigate is that of efficient sampling schemes. Estimation of EVSI relies on nested loops of random number generation, called Monte-Carlo simulation. Brute force Monte-Carlo sampling is usually too slow to be practical for EVSI. Advanced Monte-Carlo schemes that reduce the number of necessary simulations are available. These include Multilevel Monte-Carlo, which replaces the estimation target with a lower variance alternative, and Quasi Monte-Carlo, which uses quasi-random rather than random numbers to reduce variance. These have been applied to EVPPI but not EVSI; I will extend them to EVSI and furthermore to adaptive designs. Work stream 3. Application to real world cost-effectiveness models and adaptive trial designs.Practical applications include the evaluation of treatments for depression, anticoagulants for prevention of stroke in atrial fibrillation, and the comparison of prosthetics for hip replacement. MAMS trials may be considered for hip replacement due to the large number of available prosthetics and population enrichment may be applied to depression treatment due to uncertain treatment effects in low severity depression patients. Funding applications will be submitted for the most promising of these trial designs.