Category: Mixed linear regression models (July 3, 2007) . Mixed linear regression models, also known as random coefficient models extend the simple linear regression model to cases where you have to characterize variation between patients and within patients. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.

Stats: Simplifying repeated measurements (March 12, 2008). I received an email inquiry about a project that involved four repeat assessments on 10 different subjects. The question started out as, is my sample size 10 or is it 40?

Stats: The complexities of having a variable number of measures per patient (November 16, 2006). A series of messages on the MedStats email discussion group emphasized the difficulty in analyzing data where subjects contribute a variable number of measurements to the data set. If there is a relationship between the prognosis and the frequency of measurement, then you might produce some serious biases.

Stats: A simple example of a mixed linear regression model (October 18, 2006). I want to illustrate how to run a simple mixed linear regression model in SPSS. I will use some data on the plasma protein levels of turtles at baseline, after fasting 10 days, and after fasting 20 days.

Stats: (Seminar notes) Issues in the analysis of mixed linear models (July 17, 2006). The keynote address at the 18th Annual Applied Statistics in Agriculture Conference, sponsored by Kansas State University was "Random Observations with Mixed Feelings", given by Oliver Schabenberger, SAS Institute Inc. The original title was "Estimating Gene Expression Profiles Using All Available Information." Here are my notes from that seminar.

Stats: Profile analysis and MANOVA (April 18, 2005). Someone asked me about profile analysis as alternative analysis to MANOVA (Multivariate Analysis of Variance). Typically you would use profile analysis when the outcome variables are measuring (more or less) the same thing, but possibly at different times or in different ways.

Stats: Longitudinal data models (no date). Longitudinal data are data where each patient is observed on multiple occasions over time. Analysis of longitudinal data are challenging because measurements on the same subject are correlated. Another way to think about this is that two measurements on the same subject will have less variation than two measurements on different subjects.

Theme and closely related categories:

• Theme: Data analysis.
• Category: Analysis of variance
• Category: Linear regression

Other resources:

• Analysis of Longitudinal Data Description: Diggle, Liang, and Zeger's book provides an excellent overview of methods for longitudinal models which are the source of some of the greatest complexity in Statistics today. These authors, who have pioneered some of the most important work in this area, lay out both theoretical and practical information about analysis of longitudinal data. This book is for students who want more mathematical details.
• Centre for Multilevel Modelling. Description: This website describes the activities of the Centre for Multilevel Modelling, such as providing training materials and workshops, developing new methodology and collaborating with researchers in social science. This group alo produces MLwiN, a software package for fitting multilevel models.
• Cluster without fluster: The effect of correlated outcomes on inference in randomized clinical trials Description: This article discusses research studies where clusters of patients appear after the data is collected. For example, some patients in an AIDS trial invite their partners to join the study. This is in contrast to a cluster randomized trial where the clusters are defined and incorporated into a research design. Clustering that appears in a simple randomized trial does violate the assumptions that observations are independent, but under fairly mild conditions, the statistics in these studies behave reasonably.

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This webpage was written by Steve Simon on 2007-07-03, edited by Steve Simon, and was last modified on 2008-07-08. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.