Power analysis to detect treatment effect in longitudinal studies with heterogeneous errors and incomplete data.

BACKGROUND:  S. Usami (2014) describes a method to realistically determine sample size in longitudinal research using a multilevel model. The present research extends the aforementioned work to situations where it is likely that the assumption of homogeneity of the errors across groups is not met and the error term does not follow a scaled identity covariance structure. 

METHOD:  For this purpose, we followed a procedure based on transforming the variance components of the linear growth model and the parameter related to the treatment effect into specific and easily understandable indices. At the same time, we provide the appropriate statistical machinery for researchers to use when data loss is unavoidable, and changes in the expected value of the observed responses are not linear. 

RESULTS:  The empirical powers based on unknown variance components were virtually the same as the theoretical powers derived from the use of statistically processed indexes. 

CONCLUSIONS:  The main conclusion of the study is the accuracy of the proposed method to calculate sample size in the described situations with the stipulated power criteria.