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When the outcome of interest is semicontinuous and collected longitudinally,efficient testing can be difficult. Daily rainfall data is an excellent examplewhich we use to illustrate the various challenges. Even under the simplestscenario, the popular 'two-part model', which uses correlated random-effects toaccount for both the semicontinuous and longitudinal characteristics of thedata, often requires prohibitively intensive numerical integration anddifficult interpretation. Reducing data to binary (truncating continuouspositive values to equal one), while relatively straightforward, leads to apotentially substantial loss in power. We propose an alternative: using anon-parametric rank test recently proposed for joint longitudinal survivaldata. We investigate the benefits of such a test for the analysis ofsemicontinuous longitudinal data with regards to power and computationalfeasibility.

Is it even rainier in North Vancouver? A non-parametric rank-based test for semicontinuous longitudinal data