Cutaneous Human Papillomavirus Infection and Risk of Incident External Genital Le

皮肤人乳头瘤病毒感染及外生殖器事件风险

• 批准号: 8706101
• 负责人: Anna R. Giuliano
• 金额: $ 8.17万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-24 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8706101
• 关键词: Accounting; Address; Adopted; Anogenital venereal warts; Antibodies; Archives; Behavior; Benign; Biopsy; Childhood Cancer Survivor Study; Cohort Studies; Communities; Complex; Cutaneous; Data; Detection; Developing Countries; Development; Diagnosis; Disease; Epidemiologic Studies; Equation; Europe; Event; Exposure to; Funding Mechanisms; Future; Genital system; Genotype; Goals; Gold; Health; Human Papillomavirus; Human papilloma virus infection; Human papillomavirus 11; Incidence; Individual; Infection; Joints; Knowledge; Lesion; Life; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of penis; Measurement; Measures; Methods; Modeling; Morbidity - disease rate; Outcome; Parents; Participant; Pathologic; Penile Intraepithelial Neoplasia; Penis carcinoma; Policies; Population; Population Study; Prevalence; Prevention; Procedures; Process; Public Health; Recording of previous events; Recurrence; Regression Analysis; Resources; Risk; Role; Sample Size; Sampling; Serologic tests; Serum; Statistical Methods; System; Testing; Time; United States; United States National Institutes of Health; abstracting; base; carcinogenesis; carcinogenicity; clinically relevant; cohort; comparison group; condyloma; cost effective; data structure; design; follow-up; innovation; interest; men; mortality; prospective; public health relevance; repository; stem; tool; vaccine development

DESCRIPTION (provided by applicant): Abstract This application addresses the development and application of new analytic strategies for mixed recurrent-event and panel-count data. While recurrent-event data and panel-count data are both generated from recurrent-event processes, they have different observation systems. In the former, subjects are observed continuously and in the latter, subjects are observed only at discrete time points. Consequently, recurrent-event data record all occurrence times of recurrent events, while panel-count data record only the events between observation time points. It is possible that in a single cohort, some subjects have recurrent-event data while others have panel-count data, or every subject has recurrent-event data during some periods and has panel-count data during other periods. It is not unusual to have these mixed data in long-term follow-up studies. The current common practice is to approximate or simplify these complex data, resulting in potentially misleading conclusions. There is an urgent need to develop intuitive, efficient, and computationally feasible methods for analyzing complex data in event history studies. This project proposes to use data from the renown longitudinal Childhood Cancer Survivor Study (CCSS) to: 1) develop both a nonparametric estimation of the mean function and a procedure of nonparametric two-sample comparison for these mixed data; 2) Develop a semiparametric estimating equation-based method for a proportional mean model and a semiparametric estimating equation-based method for an additive rate model for regression analysis; and, 3) Extend the methods developed in Aim 2 for multivariate mixed recurrent-event and panel-count data. These approaches potentially have strong statistical and clinical relevance for the study of complex event history data.

描述（由申请人提供）：摘要本申请致力于针对混合重复事件和面板计数数据的新分析策略的开发和应用。虽然经常性事件数据和面板计数数据都是从经常性事件过程中生成的，但它们具有不同的观察系统。在前者中，受试者被连续观察，而在后者中，受试者仅在离散时间点被观察。因此，重复事件数据记录了重复事件的所有发生时间，而面板计数数据仅记录观察时间点之间的事件。有可能在单个队列中，一些受试者具有经常性事件数据，而另一些受试者具有面板计数数据，或者每个受试者在某些时期具有经常性事件数据而在其他时期具有面板计数数据。在长期随访研究中出现这些混合数据并不罕见。目前的常见做法是对这些复杂的数据进行近似或简化，从而导致可能产生误导性的结论。迫切需要开发直观、高效且计算上可行的方法来分析事件历史研究中的复杂数据。该项目建议使用著名的纵向儿童癌症幸存者研究 (CCSS) 的数据来： 1) 开发均值函数的非参数估计和这些混合数据的非参数双样本比较程序； 2）开发基于半参数估计方程的比例平均模型方法和基于半参数估计方程的加性率模型回归分析方法； 3) 将目标 2 中开发的方法扩展到多元混合经常性事件和面板计数数据。这些方法对于复杂事件历史数据的研究可能具有很强的统计和临床相关性。