Sagady, Amie Elizabeth
(2005)
AN ANALYTICAL APPROACH COMPARING REPEATED-MEASURES ANALYSIS OF VARIANCE (ANOVA) AND MIXED MODELS IN A DOUBLE BLIND PLACEBO-CONTROLLED CLINICAL TRIAL.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
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 MPH had a significant effect on the variables Withdrawn and Intensity in both of the repeated-measures analyses.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.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
8 July 2005 |
| Date Type: |
Completion |
| Defense Date: |
23 May 2005 |
| Approval Date: |
8 July 2005 |
| Submission Date: |
26 May 2005 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
longitudinal data; repeated-measures; methylphenidate; mixed models; clinical trials |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-05262005-100733/, etd-05262005-100733 |
| Date Deposited: |
10 Nov 2011 19:45 |
| Last Modified: |
19 Dec 2016 14:36 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/7952 |
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