  The National Research Council panel on prevention and treatment of missing
data in clinical trials recommends that primary analysis methods are carefully
selected before appropriate sensitivity analysis methods can be chosen. In this
paper, we recommend an appropriate primary analysis method for handling CD4
count data from the IMPI trial and trials with similar settings. The estimand
of interest in the IMPI trial is the effectiveness estimand hypothesis. We
discussed, compared, and contrasted results from complete case analysis and
simple imputation methods, with the direct-likelihood and multiple imputation
methods. The simple imputation methods produced biased estimates of treatment
effect. However, the maximum likelihood and the multiple imputation methods
produced consistent estimates of treatment effect. The maximum likelihood or
the multiple imputation approaches produced unbiased and consistent estimates.
Therefore, either the maximum likelihood or the multiple imputation methods,
under the assumption that the data are missing at random can be considered as
primary analysis method when one is considering sensitivity analysis to dropout
using the CD4 count data from the IMPI trial and other trials with similar
settings.
