Characterizing Factors that Impact the Evolution of Neurocysticercosis Cysts: A Cyst-Level Analysis Using New Statistical Methods for Complex Longitudinal Data

项目摘要 尽管有越来越多的证据表明，脑囊虫病（NC）囊肿的演变在大脑中各不相同， 被包囊的寄生虫的研究还相对不足。我们知道囊肿的进化在 脑实质和脑实质外的位置，并可能由患者的年龄和性别，但鲜为人知的是， 通过整体或囊肿和患者特征的演变阶段的过渡时间，也没有如何 囊肿和患者特征影响驱虫治疗的效果，如阿苯达唑（ALB）。我们 大多数研究仅检查患者水平的聚集体， 测量脑内NC囊肿负荷。然而，同一患者的单个NC囊肿可以演变为 不同的是，ALB可以杀死一些寄生虫，但对同一患者体内的其他寄生虫几乎没有影响;只有通过 通过观察单个囊肿，我们可以了解这些差异。因此，在这个项目中，我们的目标是分解 将患者水平的数据转换为囊肿水平，并使用多状态建模来检查单个囊肿的转换， 从疾病发展的角度来看。虽然多状态模型可以处理多变量 纵向数据，我们的NC囊肿数据提出了额外的挑战：（1）数据是区间删失，由于预 规定的数据收集时间表，（2）由于失访，数据可以进行信息右删失， 这导致可能不是随机的缺失数据，（3）数据是左删失的，因为患者输入了 研究与预先存在的囊肿和感染的时间是未知的，和（4）因为多个囊肿可以在 同一个病人，甚至在同一个大脑位置，我们有多层次的相关数据。本研究 提出了一种具有嵌套脆弱性的选择模型嵌入时齐马尔可夫多状态联合模型， NC囊肿数据。在推理方面，我们将考虑基于最大似然的方法。使用这些新 方法，我们建议进行囊肿水平的分析，以确定囊肿和病人的特点，影响 2001 - 05年随机对照试验数据显示NC囊肿演变和ALB治疗有效性 在厄瓜多尔进行，在178例患者中比较ALB治疗与安慰剂，随访时间超过24个月 在基线、第1、6、12和24个月进行脑成像（CT/MRI）。这种方法将 通过提供有关囊肿进化过程的更详细信息来推进神经囊尾蚴病的研究， 改变ALB对本课程影响的因素。这些分析的结果将增加我们对 改变ALB的有效性的因素，朝着更精确的患者发展的第一步 治疗选择，从而改善患者的结果。所提出的统计方法也可能具有 应用于对通过预定义临床状态演变并影响多个身体的其他疾病进行建模 在采用间歇性数据收集和各种形式的删失的纵向研究中进行评估时，

PROJECT SUMMARY Despite accumulating evidence that neurocysticercosis (NC) cyst evolution varies within the brain, the life course of the encysted parasite remains relatively understudied. We understand that cyst evolution differs between parenchymal and extraparenchymal brain locations and possibly by patient age and sex, but little is known about the timing of the transitions through the stages of evolution overall or by cyst and patient characteristics, nor how cyst and patient characteristics impact the effect of anthelminthic treatment such as albendazole (ALB). Our understanding of cyst evolution is hindered by the fact that most studies examine only patient-level aggregate measures of NC cyst burden within the brain. However, individual NC cysts in the same patient can evolve differently, and ALB may kill some parasites but have little effect on others within the same patient; only by following individual cysts can we understand these differences. Therefore, in this project, we aim to disaggregate patient-level data to the cyst-level and use multistate modeling to examine transitions of individual cysts through stages of evolution from the disease progress perspective. Although multistate models can handle multivariate longitudinal data, our NC cysts data pose additional challenges: (1) the data are interval-censored due to pre- specified data collection schedules, (2) the data can be informatively right-censored due to loss to follow-up, which results in missing data that may not be random, (3) the data is left-censored because patients enter a study with pre-existing cysts and the time of infection is unknown, and (4) because multiple cysts can be within the same patient, and even within the same brain location, we have multilevel correlated data. In this study, we propose a selection-model embedded time-homogeneous Markov multistate joint model with nested frailty for the NC cyst data. Inference wise, we will consider the maximum likelihood-based approach. Using these new methods, we propose to conduct cyst-level analysis to identify the cyst and patient characteristics that impact NC cyst evolution and ALB treatment effectiveness using data from a 2001-05 randomized controlled trial conducted in Ecuador that compared ALB treatment to placebo among 178 patients over 24 months follow-up with brain imaging (CT/MRI) conducted at baseline, months 1, 6, 12 and 24. This methodological approach will advance neurocysticercosis research by providing more detailed information on the cyst evolutionary course and factors that modify the impact of ALB on this course. Results from these analyses will increase our understanding of the factors that modify the effectiveness of ALB, first step towards the development of more precise patient treatment options and thereby improved patient outcomes. The proposed statistical methods may also have applications to modeling other diseases that evolve through predefined clinical states and impact multiple body regions when assessed in longitudinal studies with intermittent data collection and various forms of censoring.