• Joseph L Goulet, Eugenia Buta, Harini Bathulapalli, Ralitza Gueorguieva, and Cynthia A Brandt.
• VA Connecticut Healthcare System, West Haven, Connecticut; Yale University, School of Medicine, Department of Psychiatry, New Haven, Connecticut. Electronic address: Joseph.Goulet@VA.gov.
• J Pain. 2017 Mar 1; 18 (3): 340-348.

AbstractPain intensity is often measured in clinical and research settings using the 0 to 10 numeric rating scale (NRS). NRS scores are recorded as discrete values, and in some samples they may display a high proportion of zeroes and a right-skewed distribution. Despite this, statistical methods for normally distributed data are frequently used in the analysis of NRS data. We present results from an observational cross-sectional study examining the association of NRS scores with patient characteristics using data collected from a large cohort of 18,935 veterans in Department of Veterans Affairs care diagnosed with a potentially painful musculoskeletal disorder. The mean (variance) NRS pain was 3.0 (7.5), and 34% of patients reported no pain (NRS = 0). We compared the following statistical models for analyzing NRS scores: linear regression, generalized linear models (Poisson and negative binomial), zero-inflated and hurdle models for data with an excess of zeroes, and a cumulative logit model for ordinal data. We examined model fit, interpretability of results, and whether conclusions about the predictor effects changed across models. In this study, models that accommodate zero inflation provided a better fit than the other models. These models should be considered for the analysis of NRS data with a large proportion of zeroes.Published by Elsevier Inc.

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