Statistical models for the analysis of zero-inflated pain intensity numeric rating scale data

• Published in: The journal of pain Vol. 18; no. 3; pp. 340 - 348
• Main Authors: Goulet, Joseph L, Buta, Eugenia, Bathulapalli, Harini, Gueorguieva, Ralitza, Brandt, Cynthia A
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2017
• Subjects: Adolescent; Adult; Age Factors; Anesthesia & Perioperative Care; Cohort Studies; Female; Humans; Iraq War, 2003-2011; Male; Middle Aged; Models, Statistical; Musculoskeletal Pain - diagnosis; Musculoskeletal Pain - physiopathology; Pain; Pain Measurement - methods; Pain Medicine; statistical models; Veterans; Young Adult; zero-inflation; pain; Veterans; zero-inflation; statistical models; veterans; Pain
• ISSN: 1526-5900; 1528-8447
• DOI: 10.1016/j.jpain.2016.11.008

Abstract Pain 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 VA 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 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. Perspective We examined and analyzed pain data from a large cohort of Veterans with musculoskeletal disorders. We found that many reported no current pain on the numeric rating scale on the diagnosis date. We present several alternative statistical methods for the analysis of pain intensity data with a large proportion of zeroes.