Some alternatives in the statistical analysis of sickness absence.
OBJECTIVE
Sickness absence (SA) is a commonly used outcome in occupational health cohort studies. Without the use of statistical techniques that take into account that SA is a recurrent event, the probability of obtaining biased estimates of the effects related to SA is very high. The objective of this article is to examine the application of marginal models, comparing them to count-based models, when the outcome of interest is SA.


METHODS
By re-sampling the data of a reference study, 1,000 samples of 1,200 individuals were generated. In each of these samples, the coefficients of two factors were estimated by fitting various models: Poisson, Negative Binomial, standard Cox model for a first occurrence, Andersen-Gill and Prentice-Williams-Peterson.


RESULTS
In general, differences among the models are observed in the estimates of variances and coefficients, as well as in their distribution. Specifically, the Poisson model estimates the greatest effect for both coefficients (IRR = 1.17 and IRR = 1.60), and the Prentice-Williams-Peterson the least effect (HR = 1.01 and HR = 1.26).


CONCLUSIONS
Whenever possible, the instantaneous form of analysis should be used for occurrences of a recurrent event. Collection of study data should be organized in order to permit recording of the most complete information possible, particularly regarding event occurrences. This should allow the presence of within-individual heterogeneity and/or occurrence dependency to be studied, and would further permit the most appropriate model to be chosen. When there is occurrence dependence, the choice of a model using the specific baseline hazard seems to be appropriate.
Navarro
Albert
A
Grups de Recerca d'Amèrica i Africa Llatines, Unitat de Bioestadística, Universitat Autònoma de Barcelona, Barcelona, Spain. albert.navarro@uab.cat
Reis
Ricardo J
RJ
Martin
Miguel
M
D000041:Absenteeism; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D055809:Illness Behavior; D008297:Male; D015233:Models, Statistical; D016273:Occupational Exposure / Q000706:statistics & numerical data*; D016272:Occupational Health / Q000706:statistics & numerical data*; D016017:Odds Ratio; D016012:Poisson Distribution; D012306:Risk; D013030:Spain / Q000453:epidemiology; D013223:Statistics as Topic; D014481:United States