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

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，以处理最近的临床试验中遇到的问题，口腔健康和呼吸机相关性肺炎-一项III期随机单中心试验（批准号：1 R 01 DE 14685 - 01 A1）。在重症监护室（ICU）中，共有175名患者接受氯己定口腔冲洗治疗，每天一次或两次，或安慰剂对照，并随访至出院。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。需要进行统计调查的三个主要问题如下。1)数据磨损问题存在于关于变量的数据集中，例如口腔中的菌斑指数和细菌定植评分。在纵向研究中，实验单位的损耗是一个主要问题，目前的非参数似然方法还没有很好地解决这个问题。特别是，对于口腔健康研究，来自患者的多个结果和相应的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，其中由于检测限和测量误差而无法获得值。3)由于一个变量仅在另一个变量满足诸如超过阈值（例如，CPIS和触发BAL的收集）。 本计画提出发展统计推论方法，使用非参数似然法，在不完整资料或资料流失的情况下，检验多个群组。一些可用的参数似然（PL）方法可以解决缺失数据的问题，然而，对于不完整的数据，这些参数假设不能使用标准的拟合优度检验。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，其中考虑了缺失模式。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是非常有用的，因为常见的龋齿或牙周病数据集也存在类似的数据缺失问题。为了帮助转让方法，我们计划开发方便用户的软件。研究人员还计划在本提案的背景下，培训学生和医学研究人员正确和强大的方法来应对给定的挑战。这些方法的应用将使灵活和强大的推理在临床研究中。如果完全成功，我们相信，所提出的方法有很大的潜力，被采纳为一个主要的统计工具，改变实践的研究规划的各个临床领域。 公共卫生相关性：虽然进行临床试验是开发有益和万无一失的疾病治疗方法的必要步骤，但方法学/统计学限制往往导致研究设计和后续数据分析的灵活性降低。继续开发能够处理实际临床试验中更现实问题的统计方法势在必行。本提案的重点是针对临床试验中常见的不完整/缺失数据的新型统计方法的开发，该方法基于研究“口腔健康与呼吸机相关性肺炎-III期随机单中心试验”的口腔健康研究。

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".