We discuss the analysis of growth curve data with missing or incomplete information. The approach is to fit subject-specific models and then to carry out an analysis in terms of estimated parameters. This achieves reduction of data and eliminates the need for special considerations for subjects with missing data. Although there is no perfect substitute for complete data, our approach provides a way to handle missing data using straightforward application of well-known statistical methodology.
Johnson, W.D.; George, V.T.; Shahane, A; and Fuchs, G.J.
"Fitting Growth Curve Models to Longitudinal Data with Missing Observations,"
2, Article 9.
Available at: https://digitalcommons.wayne.edu/humbiol/vol64/iss2/9