11: Introduction to Repeated Measures

Overview Section

Studies can often be expanded by introducing time as a potential covariate. In the greenhouse example, the growth of plants can be measured weekly over a period of time, allowing time to also be included as a predictor in the statistical model. Another example is to compare the effect of two anti-cancer drugs on disease status at different intervals of time. In both these examples, the response has to be measured multiple times from the same experimental unit, hence the term ‘repeated measures’. The repeated measurements made on the same experimental unit cannot be assumed independent which means that the model errors may not be uncorrelated anymore and the statistical model should be modified accordingly.

There are two common, fundamental types of repeated measures: repeated measures in time and crossover repeated measures. In repeated measures in time designs, experimental units receive treatment and are followed with repeated measures on the response variable at several time points. In contrast, in a crossover design (studied in our next lesson), experiments involve administering all treatment levels in a sequence to each experimental unit. 

Repeated measures are frequently encountered in clinical trials including longitudinal studies, growth models, and situations in which experimental units are difficult to acquire.

Objectives

Upon completion of this lesson, you should be able to:

  • Recognize repeated measures designs in time.
  • Understand the different covariance structures that can be imposed on model error.
  • Use software such as SAS, Minitab, and R for fitting repeated measures ANOVA.