AI 中文总结
研究针对序贯多重分配随机试验（SMARTs），提出基于模拟的程序，省略强假设，通过拟合模型生成合成数据进行功效分析，可固定数据机制或效应大小来为设计提供功效，能捕捉效应大小波动，利于实现SMARTs构建治疗序列的潜力。
AI 中文摘要
序贯多重分配随机试验（SMARTs）基于患者概况为治疗序列提供证据，在慢性病环境中具有相关性。计算器中实现的样本量公式是为SMARTs提供功效的主要工具，但需要强假设。我们提出一种基于模拟的程序，省略这些假设，而是通过将模型拟合到真实试点数据来生成现实的合成SMART数据，以对比较治疗策略的SMARTs进行功效分析。所提出的框架通过两种方式为设计提供功效：通过固定数据生成机制并估计不同设计下的效应大小，或通过固定效应大小并在SMART内改变操作决策。将我们的结果与计算器（SMARTsize）进行比较，在较大的固定效应大小下，不同功效水平下估计的样本量相似，而在较小效应大小下由于模拟试验中固定效应大小与观察到的效应大小之间的差异，差异明显。基于模拟的程序捕捉这种效应大小波动的能力对于较小的预期效应大小是有利的，因为确保足够的样本量以避免II型错误至关重要。在为相互竞争的SMART设计提供灵活的功效分析工具时，可以更好地实现SMARTs构建治疗序列的全部潜力。
英文摘要
Sequential Multiple Assignment Randomized Trials (SMARTs) provide evidence for treatment sequences based on patient profiles, which is relevant in chronic disease settings. Sample size formulae implemented in calculators are the primary tool available to power SMARTs, though they require strong assumptions. We propose a simulation-based procedure omitting these assumptions, instead generating realistic synthetic SMART data by fitting models to real pilot data, to power SMARTs to compare treatment strategies. The proposed framework powers designs in two ways: by fixing the data generating mechanism and estimating effect size under different designs, or by fixing effect size and varying operational decisions within the SMART. Comparing our results to a calculator (SMARTsize), estimated sample sizes at varying power levels were similar at larger fixed effect sizes, whereas a discrepancy was apparent at smaller effect sizes due to differences between fixed and observed effect sizes in the simulated trials. The simulation-based procedure's ability to capture this effect size fluctuation is advantageous for smaller expected effect sizes, as it is essential to ensure adequate sample size to avoid a type II error. In providing flexible tools to power competing SMART designs, the full potential of SMARTs to build treatment sequences can be better realized.
Comments28 pages, 10 figures
