Analysis for Incomplete Data in Oral Health/Ventilator-Associated Pneumonia Study

口腔健康/呼吸机相关肺炎研究中不完整数据的分析

• 批准号: 8046672
• 负责人: Albert Vexler
• 金额: $ 15.85万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8046672
• 关键词: Accounting; Acute Disease; Address; Adopted; Area; Attention; Bronchoalveolar Lavage; Cessation of life; Characteristics; Chlorhexidine; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Collection; Computer software; Data; Data Analyses; Data Set; Dental Plaque Index; Dental caries; Detection; Development; Diagnosis; Diagnostic; Disease; Dropout; Grant; Guidelines; Infection; Inherited; Intensive Care Units; Investigation; Longitudinal Studies; Lung; Measurement; Measures; Medical Students; Methodology; Methods; Modeling; Non-linear Models; Oral; Oral cavity; Oral health; Organism; Outcome; Patients; Pattern; Periodontal Diseases; Phase; Placebo Control; Planning Techniques; Pneumonia; Problem Solving; Procedures; Process; Property; Randomized; Records; Research; Research Design; Research Personnel; Risk; Sample Size; Sampling; Series; Solutions; Source; Statistical Methods; Structure; Symptoms; Techniques; Testing; Tooth structure; Training; Ventilator; Visit; base; case-based; comparison group; data structure; flexibility; follow-up; indexing; instrument; interest; longitudinal analysis; microorganism; novel; novel strategies; pathogen; programs; research study; tool; user friendly software

DESCRIPTION (provided by applicant): Our proposal focuses on the development of a class of new and novel nonparametric likelihood methods for statistical inference to handle problems encountered during a recent clinical trial, Oral Health and Ventilator- Associated Pneumonia-A Phase III Randomized Single Center Trial (Grant number: 1 R01DE14685 - 01A1). A total of 175 patients in an intensive care unit (ICU) were treated with chlorhexidine oral rinse once or twice per day or with a placebo control, and followed until they were discharged. Outcome variables include oral colonization by target micro-organisms, the dental Plaque Index score and diagnostic variables for pneumonia. Three major issues that warrant the statistical investigation were as follows. 1) A data attrition problem exists in the data sets with respect to variables such as the Plaque Index and bacterial colonization scores in the oral cavity. In longitudinal studies, the attrition of the experimental units is a major problem and currently nonparametric likelihood methods have not been well developed to solve the problem. In particular, for oral health research, multiple outcomes from a patient and the corresponding correlation structure further complicate the data analysis. 2) General outcome measurements are subject to instrument sensitivity where values are not available because of the limit of detection and subject to measurement errors. 3) There is systematic missingness in the data due to the fact that one variable is observed only if the other variable satisfies a certain condition such as exceeding a threshold (e.g., CPIS and triggering collections of BAL). This project proposes the development of statistical inference methods using the nonparametric likelihood approaches to test multiple groups in the presence of incomplete data or data attrition. Some available parametric likelihood (PL) approaches can address the missing data problem, however, for incomplete data, these parametric assumptions cannot be tested using standard goodness-of-fit tests. We will develop a series of nonparametric likelihood methods relevant to the structure of incomplete data, where missing patterns are taken into account. These new methods will allow the users to avoid strong distributional assumptions by using a nonparametric approach. We will pay special attention toward utilizing the maximum information retained in the pattern of incomplete data. This novel approach will provide more powerful and accurate analyses. This approach is also immediately useful for the analysis of oral health data in general, since common dental caries or periodontal disease datasets are riddled with similar missing data problems. To help transfer of the methodology, we plan to develop user-friendly software. The investigators also plan, in the context of this proposal, to train students and medical investigators with the correct and powerful approaches to the given challenges. Application of these methods will enable flexible and powerful inference in clinical investigations. If fully successful, we believe that the proposed methods have a great potential to be adopted as a primary statistical tool that changes the practice of study planning for various clinical areas. PUBLIC HEALTH RELEVANCE: Although performing clinical trials is a necessary step to develop beneficial and fool-proof treatments for diseases, methodological/statistical limitations often cause a less flexibility in study designs and subsequent data analysis. Continuing development of statistical methods that can handle more realistic problems from actual clinical trials is imperative. This proposal focuses on the development of the novel statistical methodology for incomplete/missing data, a common problem in clinical trials, with respect to the oral health research based on the study "Oral Health and Ventilator-Associated Pneumonia-A Phase III Randomized Single Center Trial".

描述（由申请人提供）：我们的提案侧重于开发一类新的和新颖的非参数似然方法，用于统计推断，以处理在最近的临床试验中遇到的问题，口腔健康和呼吸机相关肺炎- III期随机单中心试验（批准号：1 R01DE14685 - 01A1）。175例重症监护病房（ICU）患者接受氯己定口腔冲洗治疗，每天1 - 2次或安慰剂对照，随访至出院。结果变量包括目标微生物的口腔定植，牙菌斑指数评分和肺炎的诊断变量。有必要进行统计调查的三个主要问题如下。1)数据集中存在数据损耗问题，如牙菌斑指数和口腔细菌定植评分等变量。在纵向研究中，实验单元的磨损是一个主要问题，目前非参数似然方法尚未很好地解决这一问题。特别是在口腔健康研究中，同一患者的多个结果及其相关结构进一步使数据分析复杂化。2)一般结果测量受仪器灵敏度的影响，由于检测的限制和测量误差的影响，测量值无法获得。3)数据存在系统性缺失，因为只有当一个变量满足一定的条件，如超过阈值（如CPIS和触发BAL集合），另一个变量才会被观察到。该项目提出了使用非参数似然方法来测试存在不完整数据或数据损耗的多组的统计推断方法的发展。一些可用的参数似然（PL）方法可以解决丢失数据的问题，然而，对于不完整的数据，这些参数假设无法使用标准拟合优度检验进行检验。我们将开发一系列与不完整数据结构相关的非参数似然方法，其中缺失模式被考虑在内。这些新方法将允许用户通过使用非参数方法来避免强分布假设。我们将特别注意利用不完整数据模式中保留的最大信息。这种新颖的方法将提供更强大和准确的分析。这种方法对于一般口腔健康数据的分析也立即有用，因为常见的龋齿或牙周病数据集充满了类似的缺失数据问题。为了帮助方法的转移，我们计划开发用户友好的软件。研究人员还计划，在此建议的背景下，培养学生和医学研究人员正确和有效的方法来应对给定的挑战。这些方法的应用将为临床研究提供灵活有力的推论。如果完全成功，我们相信所提出的方法有很大的潜力被采用为主要的统计工具，改变各种临床领域的研究计划实践。