title: Quantifying Selection Bias in National Institute of Health Stroke Scale Data Documented in an Acute Stroke Registry.
abstract: As a measure of stroke severity, the National Institutes of Health Stroke Scale (NIHSS) is an important predictor of patient- and hospital-level outcomes, yet is often undocumented. The purpose of this study is to quantify and correct for potential selection bias in observed NIHSS data.
  
  
  
  Data were obtained from the Michigan Stroke Registry and included 10 262 patients with ischemic stroke aged ≥65 years discharged from 23 hospitals from 2009 to 2012, of which 74.6% of patients had documented NIHSS. We estimated models predicting NIHSS documentation and NIHSS score and used the Heckman selection model to estimate a correlation coefficient (ρ) between the 2 model error terms, which quantifies the degree of selection bias in the documentation of NIHSS. The Heckman model found modest, but significant, selection bias (ρ=0.19; 95% confidence interval: 0.09, 0.29; P<0.001), indicating that because NIHSS score increased (ie, strokes were more severe), the probability of documentation also increased. We also estimated a selection bias-corrected population mean NIHSS score of 4.8, which was substantially lower than the observed mean NIHSS score of 7.4. Evidence of selection bias was also identified using hospital-level analysis, where increased NIHSS documentation was correlated with lower mean NIHSS scores (r=-0.39; P<0.001).
  
  
  
  We demonstrate modest, but important, selection bias in documented NIHSS data, which are missing more often in patients with less severe stroke. The population mean NIHSS score was overestimated by >2 points, which could significantly alter the risk profile of hospitals treating patients with ischemic stroke and subsequent hospital risk-adjusted outcomes.
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.). mthompson@uthsc.edu.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Thompson
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.).
authors.forename: Zhehui
authors.initials: Z
authors.lastname: Luo
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.).
authors.forename: Joseph
authors.initials: J
authors.lastname: Gardiner
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.).
authors.forename: James F
authors.initials: JF
authors.lastname: Burke
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.).
authors.forename: Adrienne
authors.initials: A
authors.lastname: Nickles
authors.affiliation: From the Department of Epidemiology and Biostatistics, Michigan State University, Lansing (M.P.T., Z.L., J.G., M.J.R.); Department of Neurology, University of Michigan, Ann Arbor (J.M.B.); and Chronic Disease Epidemiology Section, Michigan Department of Health and Human Services, Lansing (A.N.).
authors.forename: Mathew J
authors.initials: MJ
authors.lastname: Reeves
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000068598:Data Accuracy; D004185:Disability Evaluation; D005260:Female; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008824:Michigan; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011379:Prognosis; D012042:Registries; D015983:Selection Bias; D012720:Severity of Illness Index; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D013997:Time Factors
keywords: documentation; hospitals; registries; risk adjustment; selection bias; stroke