title: Statistical Models for the Analysis of Zero-Inflated Pain Intensity Numeric Rating Scale Data.
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 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.
  
  
  
  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 NRS on the diagnosis date. We present several alternative statistical methods for the analysis of pain intensity data with a large proportion of zeroes.
authors.affiliation: VA Connecticut Healthcare System, West Haven, Connecticut; Yale University, School of Medicine, Department of Psychiatry, New Haven, Connecticut. Electronic address: Joseph.Goulet@VA.gov.
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Goulet
authors.affiliation: VA Connecticut Healthcare System, West Haven, Connecticut; Yale University, School of Public Health, Department of Biostatistics, New Haven, Connecticut.
authors.forename: Eugenia
authors.initials: E
authors.lastname: Buta
authors.affiliation: VA Connecticut Healthcare System, West Haven, Connecticut; Yale University, School of Medicine, Department of Internal Medicine, New Haven, Connecticut.
authors.forename: Harini
authors.initials: H
authors.lastname: Bathulapalli
authors.affiliation: Yale University, School of Medicine, Department of Psychiatry, New Haven, Connecticut; Yale University, School of Public Health, Department of Biostatistics, New Haven, Connecticut.
authors.forename: Ralitza
authors.initials: R
authors.lastname: Gueorguieva
authors.affiliation: VA Connecticut Healthcare System, West Haven, Connecticut; Yale University, School of Medicine, Department of Emergency Medicine, New Haven, Connecticut.
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Brandt
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D015331:Cohort Studies; D005260:Female; D006801:Humans; D054542:Iraq War, 2003-2011; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D059352:Musculoskeletal Pain / Q000175:diagnosis* / Q000503:physiopathology; D010147:Pain Measurement / Q000379:methods*; D014728:Veterans; D055815:Young Adult
keywords: Pain; statistical models; veterans; zero-inflation