  When the outcome of interest is semicontinuous and collected longitudinally,
efficient testing can be difficult. Daily rainfall data is an excellent example
which we use to illustrate the various challenges. Even under the simplest
scenario, the popular 'two-part model', which uses correlated random-effects to
account for both the semicontinuous and longitudinal characteristics of the
data, often requires prohibitively intensive numerical integration and
difficult interpretation. Reducing data to binary (truncating continuous
positive values to equal one), while relatively straightforward, leads to a
potentially substantial loss in power. We propose an alternative: using a
non-parametric rank test recently proposed for joint longitudinal survival
data. We investigate the benefits of such a test for the analysis of
semicontinuous longitudinal data with regards to power and computational
feasibility.
