Semi-Parametric Survival Methods in Dental Research

牙科研究中的半参数生存方法

• 批准号: 7091647
• 负责人: SUNG-KIANG CHUANG
• 金额: $ 16.89万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7091647
• 关键词: clinical research; dental caries; dental implants; dental research; human data; information systems; statistics /biometry

DESCRIPTION (provided by applicant): Datasets composed of time-to-event, within-subject clustered observations are common in patient oriented dental research, and e.g. caries data are recorded from multiple teeth or even multiple surfaces per tooth from each patient. The analysis of data composed of multiple observations per patient is a challenging, yet often overlooked, proposition. Most routinely used statistical analysis procedures require the common assumption that all observations are independent. In the presence of clustered observations, these procedures are usually not directly applicable. To make valid inferences, more sophisticated statistical tools are needed in order to take within cluster correlations into account. The application of sophisticated survival method analyses in patient-oriented dental research is limited. To address this deficiency, Dr. Chuang (principal investigator) is submitting a R03 award to fund his special interests and skills in the quantitative analyses of patient-oriented dental research. This award's broad goal is to facilitate the principal investigator to develop sound and rigorous survival analytic methods with applications in dental research and in the future to function as an independent patient-oriented dental researcher, mentor and clinician. Dr. Chuang's environment is outstanding for implementing patient-oriented research. The department and institution is committed to provide individuals in patient-oriented research methods. The R03 award will provide funding for protected time with at least 35% research effort. The first specific aim of this R03 award is to identify exposures associated with implant failures and complications with a generalized linear transformation model via the marginal approach. The second specific aim is to identify exposures associated with implant failures and complications with a generalized linear transformation model via the frailty approach and to compare and contrast these two different approaches. The principal investigator has chosen this research experience because of its clinical relevance and challenging methodological and analytic issues. The analyses focus on the survival methodological issues of correlated or clustered observations with applications to dental research. The specific analytic issues addressed are generalizable to patient-oriented dental research as many studies using multiple, correlated observations per patient, i.e. probing depths, tooth loss.

描述（由申请人提供）：由事件发生时间、受试者内聚类观察结果组成的数据集在面向患者的牙科研究中很常见，例如，记录了每位患者多颗牙齿或甚至每颗牙齿多个表面的龋齿数据。分析由每个患者的多个观察结果组成的数据是一个具有挑战性的，但经常被忽视的命题。大多数常规使用的统计分析程序需要所有观察都是独立的共同假设。在存在聚集观测的情况下，这些程序通常不直接适用。为了做出有效的推断，需要更复杂的统计工具，以考虑到集群内的相关性。复杂的生存分析方法在以患者为中心的牙科研究中的应用是有限的。 为了解决这一不足，庄博士（首席研究员）正在提交R03奖，以资助他在以患者为导向的牙科研究的定量分析方面的特殊兴趣和技能。该奖项的广泛目标是促进主要研究者开发合理和严格的生存分析方法，并在牙科研究中应用，并在未来作为一个独立的以患者为导向的牙科研究人员，导师和临床医生。 庄博士的环境是优秀的实施以病人为导向的研究。 该部门和机构致力于提供以患者为导向的个人研究方法。R03奖将为至少35%的研究工作提供资金。该R03奖项的第一个具体目标是通过边际方法使用广义线性转换模型识别与植入物失效和并发症相关的暴露。第二个具体目标是通过脆弱性方法，使用广义线性转换模型识别与植入物失效和并发症相关的暴露，并比较和对比这两种方法 不同的方法。主要研究者选择这项研究经验是因为其临床相关性和具有挑战性的方法学和分析问题。分析的重点是生存的相关或聚类观察与应用牙科研究的方法学问题。所解决的具体分析问题可推广到以患者为导向的牙科研究，因为许多研究使用每个患者的多个相关观察，即探测深度，牙齿脱落。