ALTERNATIVE STATISTICAL MODELS THAT ACCOUNT FOR CLUSTERING IN DENTAL IMPLANT FAILURE DATA

[摘要] Longitudinal data analysis is a major component of public health care assessment. It is important to know how treatments compare over time, how diseases occurr and recurr, and how environmental or other exposures influence to a disease processes over time. Investigations of such topics involve the statistical analysis of time-to-event data in various areas of health care.Long term dental assessment of dental restorations have typically employed statistical analyses that assume independence of the restorations within the patient. Dental data naturally occur in the form of clusters. The patient is a cluster of correlated dental units (teeth) to be evaluated. Statistical analysis of the dental units without acknowledgement of within-cluster correlation can underestimate standard errors, which can erroneously inflate the significance level of between-cluster predictors in a model.The purpose of this thesis is to 1) review the statistical literature on the analysis of dental implant data, 2) create a suitable longitudinal data file of dental implant failure, 3) describe the data management and statistical methods used, 4) compare alternative statistical models to analyze clustered survival data, and 5) show how these models can be used to identify some patient-level and implant site-level predictors of implant failure. We consider logistic regression, discrete survival, generalized estimating equations and the Cox model with and without frailty, and examine the associations between implant failure and patient race, implant type, and oral location of implant. Models that ignore the clustering consistently overestimate the significance of patient race.

[发布机构] the University of Pittsburgh

[效力级别] dependent observations