Analyzing sickness absence with statistical models for survival data
Karl
Bang
Christensen
author
Per
Kragh
Andersen
author
Lars
Smith-Hansen
author
Martin
L
Nielsen
author
Tage
S
Kristensen
author
2007-6VL -33
text
journal article
Scandinavian Journal of Work, Environment & Health
continuing
periodical
academic journal
0355-3140
<p>OBJECTIVES: 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.</p> <p>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.</p> <p>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.</p> <p>CONCLUSIONS: An uncritical use of standard methods may underestimate the effect of work environment exposures or leave predictors of sickness absence undiscovered.</p>
frailty model
methodology
Poisson regression
proportional hazards model
register data
sickness absence
statistical model
survival data
Christensen2007
10.5271/sjweh.1132
http://www.sjweh.fi/show_abstract.php?abstract_id=1132
2007-6VL -33
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