Advertisement

Exploring Incomplete Data

Part of the Springer Series in Statistics book series (SSS)

In Chapter 4, we introduced several tools, in the context of the Vorozole study, to graphically explore longitudinal data, both from the individual-level standpoint (Figures 4.1 and 4.5) as well as from the population-averaged or group-averaged perspective (Figures 4.2, 4.3, 4.4, and 10.3). These plots are designed to focus on various structural aspects, such as the mean structure, the variance function, and the association structure.

An extra level of complexity is added whenever not all planned measurements are observed. This results in incompleteness or missingness. Another frequently encountered term is dropout, which refers to the case where all observations on a subject are obtained until a certain point in time, after which all measurements are missing.

Keywords

Stochastic Process Probability Theory Statistical Theory Longitudinal Data Variance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Verlag New York, LLC 2009

Personalised recommendations