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

TL;DR

This paper introduces a non-parametric rank-based test for analyzing semicontinuous longitudinal data, such as daily rainfall, offering a computationally feasible alternative to traditional models with improved power.

Contribution

It proposes a novel non-parametric rank test tailored for semicontinuous longitudinal data, addressing limitations of existing two-part models.

Findings

The rank test is computationally efficient.

It maintains higher statistical power than binary reduction methods.

Applicable to rainfall and similar semicontinuous data.

Abstract

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.