2006; Hesselius 2007; Koopmans et al. 2008). Revealing characteristics of employees at risk of long-term absence is important in order to reduce sickness absence, work disability and unemployment. Occupational health interventions may increase the probability of returning to work and limit economic and social deprivation associated with long-term absence. However, the impact of risk factors or interventions may vary across different stages of the sickness absence. Therefore it is important to gain insight into the time process of return to work

(Joling et al. 2006). In research on time to onset of sickness absence and the selleck duration of sickness absence episodes, Cox proportional hazards models selleck kinase inhibitor are widely used (Cheadle et al. 1994; Krause et al. 2001; Joling et al. 2006; Lund et al. 2006; Christensen et al. 2007; Blank et al. 2008). However, Cox proportional hazards models do not address the shape of the baseline hazard. The hazard is the risk of an event, for example the risk of onset of long-term sickness absence. The baseline hazard can be interpreted as the hazard function for the average individual in the sample. In Cox models, the functional form

of the baseline hazard is not given, but is determined from the data. However, the course of sickness absence and reintegration cannot be understood without knowing the baseline hazard function. One way to understand the baseline hazard second function is to specify it. For instance, it can be hypothesized that with increasing absence duration the probability of returning to work decreases in a certain pattern (Crook and Moldofsky 1994). Although Cox models leave the baseline hazard unspecified, duration dependence can be

imposed. For instance, one may assume that the baseline hazard remains constant in time or varies exponentially with time (see e.g. Bender et al. 2005). However, parametric models are preferred when time in itself is considered a meaningful independent variable and the researcher wants to be able to describe the nature of time-dependence. Different types of parametric models can be distinguished, depending on the type of time dependence of the hazard rate (Blossfeld and Rohwer 2002), as shown in Fig. 1. In exponential models, the hazard rate is assumed to be constant. Weibull models assume a hazard function that is a power function of duration. Log-logistic models permit non-monotonic hazard functions in which hazard rates can increase and then decrease or vice versa. Log-normal models are quite similar to log-logistic models, though the distribution of the error term is specified to be standard normal. Gompertz–Makeham models assume the hazard rate to be an exponential function of duration times. Fig. 1 Different parametric models for time-dependency of the hazard rate The impact of risk factors or interventions may vary in different stages of sickness absence (Krause et al. 2001).