Ye, Lei
(2015)
Semiparametric estimation procedures using local polynomial smoothing for inconsistently measured longitudinal data.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
For longitudinal data analyses, existing statistical methods can be used when the independent and dependent variables are measured at the same frequency. In Part 1 of this dissertation, we propose a three-step estimation procedure using local polynomial smoothing for longitudinal data where the variables to be handled are repeatedly measured with different frequencies within the same time period. We first inserted pseudo data for the less frequently measured variable. Then, standard linear regressions were fitted at each time point to obtain raw estimates. Lastly, local polynomial smoothing with analytical weights was applied to smooth the raw estimates. The results showed using analytic weights instead of a kernel function during smoothing is critical when the raw estimates have extreme values, or the association between the dependent and independent variables is nonlinear. In Part 2 of this dissertation, we propose another semiparametric estimation procedure to solve the same problem. After imputing pseudo data for the less frequently measured variable, local polynomial smoothing with analytical weights was applied to smooth the pseudo data for one subject at a time. Then, a suitable parametric mixed-effects model was applied. The results showed that using different types of analytic weights during smoothing produced similar results. Both proposed methods work better when the variances of the repeated measures are small and the within-subjects correlations are high. Statement of Public Health Relevance: The proposed methods are good tools for exploring inconsistently measured longitudinal data. They provide estimation without losing information that has been collected. It is important to biomedical studies especially when many researchers are using diary-based methods to improve the data collection process. For example, paper diaries, personal digital assistants (PDA) and smart phones have been used in the weight loss clinical trials to collect intensive longitudinal data that reflect subjects’ real life experiences and behaviors. The proposed methods can be used when the inconsistent measure problem is present in a longitudinal study.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
28 January 2015 |
| Date Type: |
Publication |
| Defense Date: |
4 December 2014 |
| Approval Date: |
28 January 2015 |
| Submission Date: |
23 November 2014 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
169 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Analytical weight; Inconsistent sampling; Local polynomial smoothing; Longitudinal data; Repeated measures; Semiparametric |
| Date Deposited: |
28 Jan 2015 17:20 |
| Last Modified: |
01 Jan 2017 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/23598 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[img]](https://d-scholarship.pitt.edu/style/images/fileicons/application_pdf.png)
![[feed]](/images/feed-icon-32x32.png){kind=icon}