title: Including injured workers without compensated time loss in Cox regression models: analyzing time loss using all available data.
abstract: BACKGROUND
  Cox proportional hazards regression is commonly used to analyze time loss duration, but statistical packages conventionally exclude cases with no recorded follow-up time. For this and other substantive reasons, many researchers limit time loss analyses to the subset of workers who received time loss compensation. This can exclude both injured workers who missed no work days and those missing up to a week of work. For some research questions, excluding cases where injury is reported but no time loss is recorded may result in significant ascertainment bias. We present a novel technique based on standard survival analysis methods to allow for the inclusion of all cases when appropriate.
  
  
  METHODS
  A simple technique to allow standard statistical software to include both medical-only and time loss claims in Cox regression is illustrated by example and compared with a two-part model using a time-varying step function to allow regression effects to change over time.
  
  
  RESULTS
  We showed that a pooled analysis is obtained by simply adding a small constant to the time loss duration variable. This technique produced appropriate estimates while accounting for censoring when a suitable method was used for tied event times. Using a formal statistical framework, the combined model was justified as a special case of the more standard two-part model approach.
  
  
  CONCLUSIONS
  When it is desirable to have a single pooled outcome estimate for injured workers with both medical-only and time loss claims, all claims can be combined into one statistical model. This may have particular utility for research questions where the risk factor or intervention of interest would be expected to affect time loss duration beginning upstream of claim filing or statutory compensation waiting periods. This novel alternative modeling strategy expands the tool kit available for analyzing time loss data.
authors.affiliation: Department of Health Services, School of Public Health and Community Medicine, University of Washington, Box 357660, Seattle, WA 98195, USA. jeannes@u.washington.edu
authors.forename: Jeanne M
authors.initials: JM
authors.lastname: Sears
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
mesh_terms: D006801:Humans; D053208:Kaplan-Meier Estimate; D016016:Proportional Hazards Models; D018582:Sick Leave; D014942:Workers' Compensation