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

纵向和生存数据分析的新挑战和方法

• 批准号: RGPIN-2016-03781
• 负责人: Wu, Lang
• 金额: $ 2.4万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668977
• 关键词: New; challenges; methods; analysis; longitudinal

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. ***

纵向研究在实践中广泛应用，特别是在健康相关研究中。在纵向研究中，我们随着时间的推移重复收集一组变量的数据，并且我们通常对变量的值如何随时间变化感兴趣。 *此外，我们还可能观察某些感兴趣的事件，*例如辍学和疾病感染。*在许多研究中，纵向轨迹和*事件发生的时间是相关的，因此一些纵向变量可以用作*感兴趣事件的潜在标记。例如，*在 HIV/AIDS 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相关。 *在肺癌研究中，浸润前病变的历史和进展可能与肺癌的发生有关。在这些研究中，在早期阶段检测疾病非常重要，因此主要目标是确定进展为艾滋病或癌症的标记物或风险预测因子。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，以获得*无偏结果。 ** 近年来，文献中对纵向和生存数据的联合模型进行了广泛的研究。然而，由于许多纵向研究中纵向和生存数据的复杂性，*在实践中不断出现统计分析的新挑战。 *在这些情况下，现有的统计模型和方法无法使用或不可取。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) 纵向数据可能会被审查，例如，病毒载量可能会降至艾滋病*研究中的检测限以下，或者可能是半连续的； (iii) 纵向数据可能包含测量误差、缺失数据和异常值；*(iv) 模型参数可能受到生物定律的约束； (v) 可能存在空间依赖性以及纵向依赖性； (vi)*统计推断中的计算可能非常具有挑战性。 *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。在本提案中，在几个*当前的艾滋病毒/艾滋病数据集和癌症数据集的推动下，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，这些新模型和方法可能会对艾滋病和癌症研究产生重要影响，因为它们可能会带来现有模型和方法无法获得的新见解和发现。 ***