First Advisor
Shafie, Khalil
Document Type
Dissertation
Date Created
12-1-2009
Department
College of Education and Behavioral Sciences, Applied Statistics and Research Methods, ASRM Student Work
Abstract
Longitudinal studies are commonly encountered in a variety of research areas in which the scientific interest is in the pattern of change in a response variable over time. These observations are traditionally scheduled prospectively and therefore common fixed time interval models for repeated measurements are adequate. Conversely, in informative schedule studies in which subsequent observations are scheduled on the basis of prior response outcomes, time between observations now becomes informative in the longitudinal process. Traditional fixed time approaches, however, are unable to utilize the informative nature of the data lessening the inferences achieved by these approaches. Therefore, the purpose of this research was the development of a joint model of a longitudinal process and informative time schedule data. Maximum likelihood estimates (MLE) for two special cases of the proposed model were obtained from Monte Carlo simulated data by employing the Multivariate Newton-Raphson optimization routine implemented in a SAS/IML call statements. Parameter estimates were determined for a few select cases of subject and observation length and included parameter estimates for rectangular and nonrectangular observation matrices. Finally, estimates obtained from PROC MIXED and from the proposed model were compared for accuracy and efficiency by examining their bias, variance, mean square error (MSE), and relative efficiency.
Abstract Format
html
Keywords
Gaussian; Statistics
Extent
144 pages
Local Identifiers
Bronsert_unco_0161N_10023
Rights Statement
Copyright is held by author.
Digital Origin
Born digital
Recommended Citation
Bronsert, Michael, "A joint model of a longitudinal process and informative time schedule data" (2009). Dissertations. 82.
https://digscholarship.unco.edu/dissertations/82
