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

目的：开发期望值的样本信息（EVSI）的方法，允许有效的比较传统的固定和自适应的方法的试验design.Background的经济价值：医疗保健决策者，如国家卫生与保健卓越研究所（NICE）在英国，指导他们的决策成本效益分析。这些是对疾病干预措施的成本和效果进行的基于试验或模型的比较。消除这些决策建议中的不确定性的价值是完美信息的期望值（EVPI）。仅消除不确定性子集的值是部分完美信息的期望值（EVPPI）。最后，EVSI是通过特定的试验设计减少而不是消除不确定性的经济价值。适应性设计是那些随着结果的积累而适应的设计。示例包括：减少或增加治疗组的多组多阶段（MAMS）试验;改变随机化比例的试验;以及集中招募最有可能获益或获益最不确定的患者的人群富集试验。这种设计可以达到相同的准确度和精确度水平，但分配给不良或有害治疗的患者较少，总体样本量较小，持续时间较短或试验成本较低。自适应设计还可以测试多个假设，可能提供更好的投资回报。自适应设计越来越受欢迎，但仍然是例外而不是规则。EVSI仅应用于有限范围的自适应设计，计算负担是更广泛使用的主要障碍。我将扩展和开发计算效率高的EVSI方法，以适应性设计，以评估其与传统设计相比的经济价值。将估计EVSI的有效方法扩展到自适应设计。当成本效益分析依赖于复杂的模型，如马尔可夫多状态模型，估计EVSI的任何试验可以代表一个巨大的计算挑战。这对于自适应设计尤其如此。最近的工作集中在方法来近似的成本效益模型，包括通过灵活的高斯过程或广义加法模型回归，近似通过样条或泰勒级数，和矩匹配的成本和效果分布。这些已经被探索用于评估固定设计而不是自适应设计。我将把这些方法扩展到评估不同适应性设计的经济效益。发展先进的蒙地卡罗抽样方案来估计适应性设计的EVSI。我将研究的第二种方法是有效抽样方案。EVSI的估计依赖于随机数生成的嵌套循环，称为蒙特-卡罗模拟。蛮力蒙特-卡罗采样通常太慢，对EVSI不实用。先进的蒙特-卡罗计划，减少了必要的模拟的数量。这些方法包括多级蒙特-卡罗法，它用较低的方差替代估计目标，以及准蒙特-卡罗法，它使用准随机数而不是随机数来减少方差。这些已经应用于EVPPI，但不是EVSI;我将把它们扩展到EVSI，并进一步扩展到自适应设计。工作流程3。应用于真实的世界成本效益模型和适应性试验设计。实际应用包括抑郁症治疗的评估，预防房颤中风的抗凝剂，以及髋关节置换术的假体比较。MAMS试验可考虑用于髋关节置换术，因为有大量可用的假体，并且由于低严重度抑郁症患者的治疗效果不确定，可将人群富集应用于抑郁症治疗。将为这些试验设计中最有前途的设计提交资金申请。

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.