Abstract：
BACKGROUND: Our randomized controlled clinical trial will explore the potential of bazedoxifene plus conjugated estrogen to modulate breast tissue-based risk biomarkers as a surrogate for breast cancer risk reduction. This paper investigates the statistical design features of the trial and the rationale for the final choice of its design. Group sequential designs are a popular design approach to allow a trial to stop early for success or futility, potentially saving time and money over a fixed trial design. While Bayesian adaptive designs enjoy the same properties as group sequential designs, they have the added benefit of using prior information as well as inferential interpretation conditional on the data. Whether a frequentist or Bayesian trial, most adaptive designs have interim analyses that allow for early stopping, typically utilizing only the primary endpoint. A drawback to this approach is that the study may not have enough data for adequate comparisons of a single, key secondary endpoint. This can happen, for example, if the secondary endpoint has a smaller effect than the primary endpoint.
METHODS: In this paper, we investigate a trial design called two-endpoint adaptive, which stops early only if a criterion is met for primary and secondary endpoints. The approach focuses the final analysis on the primary endpoint but ensures adequate data for the secondary analysis. Our study has two arms with a primary (change in mammographic fibroglandular volume) and secondary endpoint (change in mammary tissue Ki-67).
RESULTS: We present operating characteristics including power, trial duration, and type I error rate and discuss the value and risks of modeling Bayesian group sequential designs with primary and secondary endpoints, comparing against alternative designs. The results indicate that the two-endpoint adaptive design has better operating characteristics than competing designs if one is concerned about having adequate information for a key secondary endpoint.
CONCLUSIONS: Our approach balances trial speed and the need for information on the single, key secondary endpoint.
METHODS: In this paper, we investigate a trial design called two-endpoint adaptive, which stops early only if a criterion is met for primary and secondary endpoints. The approach focuses the final analysis on the primary endpoint but ensures adequate data for the secondary analysis. Our study has two arms with a primary (change in mammographic fibroglandular volume) and secondary endpoint (change in mammary tissue Ki-67).
RESULTS: We present operating characteristics including power, trial duration, and type I error rate and discuss the value and risks of modeling Bayesian group sequential designs with primary and secondary endpoints, comparing against alternative designs. The results indicate that the two-endpoint adaptive design has better operating characteristics than competing designs if one is concerned about having adequate information for a key secondary endpoint.
CONCLUSIONS: Our approach balances trial speed and the need for information on the single, key secondary endpoint.
摘要：
背景：我们的随机对照临床试验将探索bazedoxifene联合雌激素调节基于乳腺组织的风险生物标志物作为降低乳腺癌风险的替代药物的潜力。本文研究了试验的统计设计特征以及最终选择其设计的理由。小组顺序设计是一种流行的设计方法，可以让试验尽早停止，以获得成功或徒劳。可能比固定的试验设计节省时间和金钱。虽然贝叶斯自适应设计享有与组顺序设计相同的属性，他们有额外的好处，使用先验信息以及推理解释条件的数据。无论是频率学家还是贝叶斯试验，大多数自适应设计都有临时分析，可以提前停止，通常只利用主端点。这种方法的一个缺点是，该研究可能没有足够的数据来充分比较单个，关键次要端点。这可能发生,例如,如果次要端点的影响小于主要端点。
方法：在本文中，我们研究了一种名为双端点自适应的试验设计，只有在满足主要和次要端点的条件时，才会提前停止。该方法将最终分析集中在主要终点，但确保二次分析有足够的数据。我们的研究有两个臂，主要（乳房X线摄影纤维腺体积的变化）和次要终点（乳腺组织Ki-67的变化）。
结果：我们介绍了包括功率，试验持续时间,和I型错误率，并讨论了具有主要和次要端点的贝叶斯群序贯设计建模的价值和风险，与替代设计进行比较。结果表明，如果关注关键次要端点的足够信息，则两端点自适应设计比竞争设计具有更好的操作特性。
结论：我们的方法平衡了试验速度和对单一信息的需求，关键次要端点。
方法：在本文中，我们研究了一种名为双端点自适应的试验设计，只有在满足主要和次要端点的条件时，才会提前停止。该方法将最终分析集中在主要终点，但确保二次分析有足够的数据。我们的研究有两个臂，主要（乳房X线摄影纤维腺体积的变化）和次要终点（乳腺组织Ki-67的变化）。
结果：我们介绍了包括功率，试验持续时间,和I型错误率，并讨论了具有主要和次要端点的贝叶斯群序贯设计建模的价值和风险，与替代设计进行比较。结果表明，如果关注关键次要端点的足够信息，则两端点自适应设计比竞争设计具有更好的操作特性。
结论：我们的方法平衡了试验速度和对单一信息的需求，关键次要端点。
