Mthods For Missing and auxiliary Data in clinical trials

随机临床试验现在是，也将继续是评估新的和现有的癌症疗法的关键载体。生物科学进步的这个革命性时代正在导致新的生物标记物和复杂的遗传和基因组信息的发现，这些信息可能与各种临床结果高度相关，这为利用这些信息来提高试验分析的精度和开发可能揭示重要见解的纵向疾病进展模型提供了诱人的机会。一个反复出现的挑战是，数据缺失和受试者退出是司空见惯的，这给这些试验的分析带来了复杂性。通过一系列旨在解决这些问题的目标，该项目 提出的研究将对当前癌症临床试验可能得出的推论的质量和强度产生重大影响。通过利用预后基线辅助信息来提高临床试验的初步分析的效率是众所周知的；然而，这种分析是有争议的，因为选择导致最显著治疗效果的分析是有诱惑力的。在第一个目标中，将研究这种“协变量调整”的新方法，以绕过这个问题，并能够改进现有的方法。在第二个目标中，这些方法将被扩展，以便它们可以用于 由于辍学而遗漏结果的常见情况。还将开发有效的方法，在辍学的情况下对生活质量和生物标记物等措施进行纵向分析。 了解此类纵向测量与临床结果(如复发时间或生存时间)之间的关系至关重要。第三个目标侧重于制定评估用于此目的的所谓联合统计模型的正确性的方法，以及评估特定观测对模型适用性的影响的方法，在这种情况下，用于开发模型的数据可能会丢失。最后，适当考虑丢失的数据有时需要对数据丢失的原因作出不可验证的假设，这些假设被合并到无法根据数据进行检查的模型中。第四个目标是制定一个新的统计框架，以评估结论对所作建模假设的敏感性。

Randomized clinical trials are and will continue to be the key vehicle for evaluation of new and existing cancer therapies. This revolutionary era of advances in the biological sciences is leading to the discovery of novel biomarkers and complex genetic and genomic information that may be highly associated with various clinical outcomes, offering the tantalizing opportunity to exploit this information to both improve the precision of the analyses of trials and to develop models of longitudinal disease progression that may reveal important insights. A recurrent challenge is that missing data and subject drop-out are commonplace, presenting complications for analyses of these trials. Through a series of aims addressing these issues, this project proposes research that will have a significant impact on the quality and strength of inferences possible from current cancer clinical trials. That it is possible to improve efficiency of primary analyses of clinical trials by exploiting prognostic baseline auxiliary information is well known; however, such analyses are controversial because of the temptation to choose the analysis that leads to the most dramatic treatment effect. In the first aim, new methods for such "covariate adjustment" will be studied that circumvent this issue and can improve over existing approaches. In the second aim, these methods will be extended so that they may be used in the common case where outcomes are missing due to drop-out. Efficient methods for longitudinal analysis of measures such as quality of life and biomarkers in the presence of drop-out will also be developed. Understanding the relationship between such longitudinal measures and clinical outcomes such as time to recurrence or survival time is of key importance. The third aim focuses on development of methods for assessing the correctness of so-called joint statistical models used for this purpose and for assessing the influence of particular observations on the fit ofthe model, where the data used to develop the model may be missing. Finally, taking appropriate account of missing data sometimes requires unverifiable assumptions about why the data are missing, which are incorporated in models that thus cannot be checked based on the data. The fourth aim is devoted to development of a new statistical framework for assessing how sensitive conclusions are to the modeling assumptions made.