1994 Fiscal Year Final Research Report Summary
Estimation of idealized aging curve of clinical measurements based on semi-longitudinal data
| Project/Area Number |
05680252
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | HIROSHIMA UNIVERSITY |
|---|
Principal Investigator |
OHTAKI Megu Research Institute for Radiation Biology and Medicine, Hiroshima University Professor, 原爆放射能医学研究所, 教授 (20110463)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SHIMOKATA Hiroshi Research Institute for Radiation Biology and Medicine, Hiroshima University Asoc, 原爆放射能医学研究所, 助教授 (10226269)
|
|---|
| Project Period (FY) |
1993 – 1994
|
| Keywords | Growth cuve model / Repeated measurements / Semi-longitudinal study / Smoothing / Aging curve |
|---|
| Research Abstract |
As for theoretical results, we developed a growth curve model, which is an extension of the model due to Vonesh and Carter (1987, Biometrics), to achieve effective analyzes to unblanced repeated measurements (Ohtaki, 1994, J.J.Biometrics). In the extended growth curve models, we reduced the number of unknown parameters by introducing a family of linear structire into the fixed location and the variance-covariance parameters so that the resultant models can provide higher efficiency and easier interpretation in analysis. It was shown that the family of the extended models contains not only the ordinal growth curve model with non-specified structure for parameters but also many useful growth curve models such as "parallel profile model". We have also developed a computer algorithm and a software on PC for estimating idealized aging curve with repeated observations from semi-longitudinal study. The algorithm consists of four steps : the first step is fitting a regression line for each individual observations to obtain annual changing rate of repeated measurements, the second one is non-parametric smoothing the annual changing rates, the third one is numerical integration of the smoothed annual changing rate for estimating mean struture of aging trend, the last step is analyzing the residuals through growth curve models for estimating the variance-covariance of the random components of repeated measurements. The softare is coded in Fortran 77, and the compiled program is available on PC with MS-DOS ver.5/ver.6. As a pratical application of our method for estimating idealized aging curve, we have studied on the age-related change of clinical measurements such as height, the level of Plasma Choresterol and Urinary Acid in Japanese populations (Kuzuya and Shimokata, 1995, Atherosclerosis ; Ohtaki, Shimokata et al., 1996, Proc.of RIRBM).
|
