New challenges and methods for the analysis of longitudinal and survival data

纵向研究在实践中被广泛应用，特别是在与健康相关的研究中。在纵向上 研究中，我们反复收集一组变量的数据， 我们经常感兴趣的是变量的值如何随时间变化。 此外，我们还可能观察到某些感兴趣的事件， 例如辍学和疾病感染。 在许多研究中，纵向轨迹和时间 这些事件是关联的，因此可以使用一些纵向变量作为 感兴趣事件的潜在标记。例如, 在艾滋病毒/艾滋病研究中，CD4和病毒载量数据通常是随着时间的推移而收集的， 它们的纵向轨迹可能与艾滋病或死亡的时间有关。 在肺癌研究中，侵袭前病变的病史和进展可能与此有关。 与肺癌的发生有关。在这些研究中，及早发现疾病是很重要的 因此，一个主要的目标是确定进展的标志或风险预测因素 爱滋病或癌症。对于这些数据的统计分析， 需要纵向数据和生存数据的联合模型才能获得 结果不偏不倚。在文献中已经进行了广泛的研究 近几年纵向和生存数据的联合模型。然而， 统计分析方面的新挑战在实践中不断出现，原因是 许多纵向研究中纵向数据和生存数据的复杂性。 在这些情况下，现有的统计模型和方法不能使用或不受欢迎。例如，(I) 感兴趣的变量可以是离散的或连续的或其他混合类型；(Ii)纵向数据可以被审查，例如，艾滋病病毒载量可能降至检测下限以下 研究，或可能是半连续的；(Iii)纵向数据可能包含测量误差、缺失数据和离群值； (4)模型参数可能受到生物规律的约束；(5)既可能存在空间相关性，也可能存在纵向相关性；以及(6) 统计推断中的计算可能是相当具有挑战性的。 因此，需要开发新的或更好的统计模型和方法来解决这些问题。在这项提案中，出于几个原因 目前的HIV/AIDS数据集和癌症数据集，我们建议开发一系列新的联合模型和方法来 解决上述问题。一旦开发出来，这些新的模型和方法可能会有重要的 对艾滋病和癌症研究的影响，因为它们可能带来基于现有模型和方法无法获得的新见解和新发现。

Longitudinal studies are widely used in practice, especially in health related research. In a longitudinal study, we collect data on a set of variables repeatedly over time, and we are often interested in how the values of the variables change over time. In addition, we may also observe certain events of interest, such as dropouts and infection of a disease. In many studies, the longitudinal trajectories and the times to the events are associated, so some longitudinal variables may be used as potential markers for the events of interest. For example, in HIV/AIDS studies, CD4 and viral load data are usually collected over time, and their longitudinal trajectories may be associated with times to AIDS or deaths. In lung cancer studies, the histories and progressions of pre-invasive lesions may be associated with occurrences of lung cancer. In these studies, it is important to detect the disease in early stages, so a major goal is to identify markers or risk predictors for progression to AIDS or cancer. For statistical analyses of these data, joint models of longitudinal and survival data are required in order to obtain unbiased results. There has been extensive research in the literature for joint models of longitudinal and survival data in recent years. However, new challenges in statistical analysis continue to arise in practice, due to the complexity of longitudinal and survival data in many longitudinal studies. In these cases, existing statistical models and methods cannot be used or undesirable. For example, (i) the variables of interest may be discrete or continuous or other mixed types; (ii) the longitudinal data may be censored, e.g., viral load may drop below a limit of detection in AIDS studies, or may be semi-continuous; (iii) the longitudinal data may contain measurement errors, missing data, and outliers; (iv) the model parameters may be constrained by biological laws; (v) there may be spatial dependence as well as longitudinal dependence; and (vi) the computation in statistical inference can be quite challenging. Thus, new or better statistical models and methods need to be developed to address these issues. In this proposal, motivated by several current HIV/AIDS datasets and cancer datasets, we propose to develop a series of new joint models and methods to address the foregoing issues. Once developed, these new models and methods may have important impacts in AIDS and cancer research, since they may lead to new insights and findings which are unavailable based on existing models and methods.