Longitudinal studies are common in many areas of public health. A usual method to analyze longitudinal data is by repeated-measures analysis of variance (ANOVA). A newer method, the mixed models approach, is gaining more acceptance due to the available use of computer programs. It is of public health importance to review the advantages of the recent mixed models approach to analyzing longitudinal data.
The main characteristic of longitudinal studies is that the outcome of interest is measured on the same individual at several points in time. The standard approach to analyzing this type of data is the repeated-measures ANOVA, but this type of design assumes equal correlation between individuals and either includes data from individuals with complete observations only or imputes missing data, both of which suffer from the ineffective use of available data. Alternatively, the mixed model approach has the ability to model the data more accurately because it can take into account the correlation between repeated observations, as well as uses data from all individuals regardless of whether their data are complete.
This thesis first reviews the literature on the repeated-measures ANOVA and mixed models techniques. Data from a placebo-controlled clinical trial of the drug methylphenidate (MPH) looking at the social/play behavior of children with attention deficit hyperactivity disorder (ADHD) and mental retardation (MR) are analyzed using repeated-measures ANOVA, repeated-measures ANOVA with the last observation carried forward (LOCF) and mixed models techniques. P-values and parameter estimates for the three methods are compared.
MPH had a significant effect on the variables Withdrawn and Intensity in both of the repeated-measures analyses. With the repeated-measures with LOCF, MPH had a significant effect on the variables Activity Intensity Level and Sociability. The mixed models analysis found MPH to have a significant effect on the variables Intensity and Activity Intensity Level. The parameter estimates for the two repeated-measures ANOVA analyses were almost identical, but the mixed model parameter estimates were different. Mixed models should be used to analyze these data as assumptions of the repeated-measures ANOVA are violated. Mixed models also take into account the missing data and correlated outcomes.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-05262005-100733 |
| Date | 08 July 2005 |
| Creators | Sagady, Amie Elizabeth |
| Contributors | Ada Youk, Sheryl Kelsey, Benjamin Handen |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-05262005-100733/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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