Is it even rainier in North Vancouver? A non-parametric rank-based test for semicontinuous longitudinal 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 potential benefits of such a test for the analysis of semicontinuous longitudinal data with regards to power and computational feasibility.

KEYWORDS:

• Semicontinuous data
• rank test
• longitudinal
• tobit model
• resampling methods

Acknowledgements

Thank you to Dr. Kahlil Baker for the friendly and thought-provoking computational assistance. Thank you to Professors Lang Wu, Nancy Heckman, and Paul Gustafson for the thoughtful discussions and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Harlan Campbell http://orcid.org/0000-0002-0959-1594