title: Analyzing sickness absence with statistical models for survival data.
abstract: OBJECTIVE
  Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data.
  
  
  METHODS
  Three methods for analyzing data on sickness absences were compared using a simulation study involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation between the psychosocial work environment and sickness absence were used to illustrate the results.
  
  
  RESULTS
  Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models.
  
  
  CONCLUSIONS
  An uncritical use of standard methods may underestimate the effect of work environment exposures or leave predictors of sickness absence undiscovered.
authors.affiliation: National Research Centre for the Working Environment, Lersø Parkallé 105, 2100, Copenhagen Ø, Denmark. kbc@nrcwe.dk
authors.forename: Karl Bang
authors.initials: KB
authors.lastname: Christensen
authors.forename: Per Kragh
authors.initials: PK
authors.lastname: Andersen
authors.forename: Lars
authors.initials: L
authors.lastname: Smith-Hansen
authors.forename: Martin L
authors.initials: ML
authors.lastname: Nielsen
authors.forename: Tage S
authors.initials: TS
authors.lastname: Kristensen
mesh_terms: D000041:Absenteeism; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018582:Sick Leave / Q000706:statistics & numerical data*; D016019:Survival Analysis