Sensitivity analyses

While our main analysis is based on robust techniques, we also carried out several sensitivity analyses.

First, the study was subject to substantial dropout and this may have affected the cost estimates at t₁ and t₂. In the main analysis, the intention-to-treat analysis was performed using regression imputation (RI) of missing observations at follow-up. In the sensitivity analysis, the intention-to-treat analysis was repeated using linear mixed modelling (LMM), to see if the costs followed the same trajectory over time as estimated under the main analysis.

Second, the main analysis was conducted without adjusting for baseline costs of presenteeism and absenteeism, but there was a small (and statistically insignificant) difference between both conditions. Hence, the main analysis was repeated including costs of presenteeism and absenteeism at baseline as a covariate to adjust for this slight baseline imbalance.

Third, given the relatively high dropout, a sensitivity analysis was conducted in which missing observations were imputed using multiple imputations (5 times) by chained equations with predictive mean matching in which ‘‘real’’ observed values from similar cases are imputed instead of imputing regression estimates. This technique is often used to account for non-normality of data which is often the case for costs.

Last, cumulative costs in the base case were calculated on a relatively conservative basis, where the costs at baseline were assumed to be stable for the first 8 weeks (in favour of waitlisted control condition). Hence, in a sensitivity analysis, cumulative costs were calculated using the following formula: CC=5C0+C1/2+C1+5C1+C2/2+C2..