  Many observational studies feature irregular longitudinal data, where the
observation times are not common across individuals in the study. Further, the
observation times may be related to the longitudinal outcome. In this setting,
failing to account for the informative observation process may result in biased
causal estimates. This can be coupled with other sources of bias, including
non-randomized treatment assignments and informative censoring. This paper
provides an overview of a flexible weighting method used to adjust for
informative observation processes and non-randomized treatment assignments. We
investigate the sensitivity of the flexible weighting method to violations of
the noninformative censoring assumption, examine variable selection for the
observation process weighting model, known as inverse intensity weighting, and
look at the impacts of weight trimming for the flexible weighting model. We
show that the flexible weighting method is sensitive to violations of the
noninformative censoring assumption and show that a previously proposed
extension fails under such violations. We also show that variables confounding
the observation and outcome processes should always be included in the
observation intensity model. Finally, we show that weight trimming should be
applied in the flexible weighting model when the treatment assignment process
is highly informative and driving the extreme weights. We conclude with an
application of the methodology to a real data set to examine the impacts of
household water sources on malaria diagnoses.
