Design and Analysis of Palliative Care Trials Evaluating Early Interventions

描述(申请人提供)：姑息治疗专注于帮助患者缓解疼痛和防止痛苦，特别是那些面临晚期癌症和其他危及生命的疾病的患者。超过80%的美国大型医院已经或正在开发姑息治疗计划。虽然姑息治疗的主要目标是改善患者的结果，如疼痛和生活质量(QOL)，但研究表明，姑息治疗也可能影响生存。评估和改进姑息治疗计划是这些研究研究的主要目标。为了实现这一目标，需要新的统计方法，从改进过程和干预措施的定义到结果的标准化和改进的统计分析。在这个项目中，我们建议开发新的方法来设计和分析姑息治疗研究研究，这些方法有可能增加科学严谨性，并充分利用这些试验的宝贵数据 生成。姑息治疗研究中提出的主要统计学问题之一是关于生活质量和持续时间之间的相互作用和权衡，通常用作主要结果。人们普遍注意到，晚期癌症患者在生命末期的预后会恶化。这表明存活率和患者预后的纵向测量之间存在相关性。姑息治疗干预研究通过显示生存和生活质量以及包括疼痛在内的症状的改善，强化了这一假设。然而，在分析或设计姑息治疗临床试验时没有考虑这种依赖性。我们最近开发了一种方法，在姑息护理研究中使用一种新的终端下降模型(TDM)对纵向结果和生存进行联合建模，其中结果是根据从死亡到死亡的时间在回溯时间尺度上建模。随机“早期干预”或“快速通道”试验(有时也称为延迟干预、延迟启动或 等待名单设计)已被提议作为传统随机对照试验的替代方案，将姑息治疗干预措施与非姑息或其他支持性护理小组进行比较。然而，在实施这种设计方面仍然存在许多问题。我们建议在TDM的背景下研究这种试验设计和更一般的“阶梯楔形”设计。具体地说，我们将讨论评估治疗效果和选择最佳设计特征的方法，如总体样本量和干预的最佳时机。

DESCRIPTION (provided by applicant): Palliative care focuses on helping relieve pain and prevent the suffering of patients, especially those patients facing advanced cancer and other life-threatening illnesses. More than 80% of large U.S. hospitals have or currently are developing palliative care programs. Although the main goal of palliative care is to improve patient outcomes such as pain and quality of life (QoL), studies have demonstrated that palliative care may also affect survival. Evaluation and improvement of palliative care programs is a primary goal for these research studies. To accomplish this goal, new statistical methods are needed, ranging from improved definition of processes and interventions to standardization of outcomes and improved statistical analysis. In this project, we propose to develop novel methods for the design and analysis of palliative care research studies that have the potential to increase scientific rigor and to fully utilize the valuable data that these trials generate. One of the major statistical issues raised in palliative care studies regards the interplay and tradeoffs between the quality and duration of life, commonly used as primary outcomes. It has been widely noted that patient outcomes worsen at the end of life for patients with advanced cancer. This suggests dependence between survival and longitudinal measurements of patient outcomes. Palliative care intervention studies have reinforced this hypothesis by showing gains in survival and QoL and symptoms including pain. However, this dependence has not been taken into account in analyzing or designing palliative care clinical trials. We have recently developed a method for jointly modeling longitudinal outcomes and survival in palliative care studies using a novel terminal decline model (TDM) where outcomes are modeled in terms of time from death on a retrospective time scale. Randomized "early intervention" or "fast-track" trials (also sometimes called delayed intervention, delayed-start, or wait list designs) have been proposed as an alternative to traditional RCTs comparing palliative care interventions to non-palliative or other supportive care groups. However, many issues remain with respect to implementing such designs. We propose to study this trial design and more general "stepped wedge" designs in the context of TDMs. Specifically, we will address methods for estimating treatment effects and for choosing optimal design features such as overall sample size and the optimal timing of interventions..