Statistical Methods for Analyzing Data of Recurrent Infections after Hematopoieti

造血后复发感染数据分析的统计方法

• 批准号: 8761909
• 负责人: Xianghua Luo
• 金额: $ 7.16万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761909
• 关键词: Accounting; Biological; Characteristics; Data; Data Analyses; Data Set; Disease; Engraftment; Enrollment; Event; Failure; Goals; Hematopoietic stem cells; Immune; Infection; Joints; Language; Lead; Longitudinal Studies; Methodology; Methods; Minnesota; Modeling; Morbidity - disease rate; Natural History; Patient Care; Patients; Population; Randomized; Recovery; Recurrence; Research; Risk; Risk Factors; Sampling; Selection Bias; Source; Statistical Methods; Survival Analysis; The Sun; Time; Transplant Recipients; Transplantation; Umbilical Cord Blood; Universities; Writing; design; experience; follow-up; hematopoietic cell transplantation; improved; innovation; interest; mortality; programs; public health relevance; randomized trial; simulation; time use; user-friendly

DESCRIPTION (provided by applicant): The overall goal of this project is to develop statistically proper and efficient methods for analyzing the gap times between recurrent infections after hematopoietic cell transplantation (HCT). Infection is one of the most common problems after HCT and accounts for substantial morbidity and mortality. Many patients experience infectious complications repeatedly over time. To characterize the natural history of infectious complications after transplantation and to identify risk factors related to infections, ne needs innovative multivariate statistical methods which can efficiently use the time information of recurrent infectious events and the rich data of patient and transplant related characteristics routinely collected by transplant centers. Existing statistical methods for recurrent gap time data typically assume that patients are enrolled due to the occurrence of an event of the same type as the recurrent event or assume that all gap times, including the first gap, are identically distributed. Applying these methods on the post-transplant infection data, thus ignore event types, will inevitably lead to incorrect inferential results because the time from the transplant t the first infection has a different biological meaning than the gap times between recurrent infections after the first infection. Alternatively, one may only analyze data after the first infetion to make the existing recurrent gap time methods applicable, but this introduces selection bias because only patients who have experienced infections are included in the analysis. Other naive methods may include using the univariate survival analysis methods, e.g., the Kaplan-Meier method and the Cox regression model, on the first infection only data or using the bivariate survival data methods, e.g., the Huang-Louis estimator and the Lin-Sun-Ying estimator, on the data up to the second infections. Hence, all subsequent infection data beyond the first or the second infectious events will not be utilized in the analysis, which will lead to inefficient estimation or a decreased power. In this application, we propose to develop efficient statistical methods for analyzing the gap times between recurrent infections after transplant. In Specific Aim 1, we will develop the nonparametric estimation method for the joint distribution of the time from the transplant to the first infection and the gap times between recurrent infections for the population of interest. In Specific Aim 2, we will develop regression model, which can incorporate patient and treatment characteristics to study the risk factors of infectious complications of the transplant patients. All proposed methods will be evaluated using extensive simulation studies and the analysis of an existing data set collected from patients who received their first HCT from the University of Minnesota between January 1, 2000 and December 31, 2010. User-friendly programs written in R language will be developed for the proposed methods and made freely available for public use. The proposed research holds both methodological significance and scientific significance. First, the methodology will be generally applicable to other recurrent gap time data with the initial event being different from all the subsequent events, which design is frequently encountered in longitudinal studies. Second, the proposed research holds the potential to advance the understanding of the natural history of infectious complications after hematopoietic cell transplantation and to help identify risk factors and improve the post- transplant care of patients.

描述（由申请人提供）：本项目的总体目标是开发统计学上适当和有效的方法，用于分析造血细胞移植（HCT）后复发感染之间的差距时间。感染是HCT后最常见的问题之一，并占相当大的发病率和死亡率。随着时间的推移，许多患者反复出现感染性并发症。为了表征移植后感染并发症的自然史并识别与感染相关的风险因素，需要创新的多元统计方法，该方法可以有效地利用复发感染事件的时间信息以及移植中心常规收集的患者和移植相关特征的丰富数据。用于经常性间隙时间数据的现有统计方法 通常假设患者是由于发生与复发事件相同类型的事件而被招募的，或者假设所有间隙时间（包括第一间隙）是相同分布的。将这些方法应用于移植后感染数据，从而忽略事件类型，将不可避免地导致不正确的推断结果，因为从移植到首次感染的时间与首次感染后复发感染之间的差距时间具有不同的生物学意义。或者，可以仅分析第一次感染后的数据，以使现有的复发间隔时间方法适用，但这会引入选择偏倚，因为只有经历过感染的患者才被纳入分析。其他朴素方法可以包括使用单变量生存分析方法，例如，Kaplan-Meier方法和考克斯回归模型，仅首次感染数据或使用双变量生存数据方法，例如，Huang-Louis估计量和Lin-Sun-Ying估计量。因此，在分析中将不利用第一或第二感染事件之外的所有后续感染数据，这将导致低效估计或功效降低。 在本申请中，我们建议开发有效的统计方法，用于分析移植后复发感染之间的差距时间。在具体目标1中，我们将开发从移植到首次感染的时间和目标人群复发感染之间的差距时间的联合分布的非参数估计方法。在具体目标2中，我们将开发回归模型，该模型可以结合患者和治疗特征来研究移植患者感染并发症的风险因素。将使用广泛的模拟研究和对从2000年1月1日至2010年12月31日期间从明尼苏达大学接受首次HCT的患者收集的现有数据集的分析，对所有提出的方法进行评价。用R语言编写的用户友好的程序将为拟议的方法开发，并免费提供给公众使用。 本研究具有方法论意义和科学意义。首先，该方法将普遍适用于其他经常性的间隙时间数据的初始事件是不同的所有后续事件，这种设计是经常遇到的纵向研究。其次，拟议的研究有可能促进对造血细胞移植后感染并发症自然史的理解，并有助于识别风险因素和改善患者的移植后护理。