Andersson et al. BMC Public Health 2011, 11:215 http://www.biomedcentral.com/1471-2458/11/215 Table 1 Demographics Sex Age Male (n) Female (n) mean years (sd) min-max years Years since diagnosis mean years (sd) min-max years Total (n = 85) 13 72 34.6 (9.4) 20-61 6.3 (7.3) 0-41 CBT (n = 42) 36.4 (10.1) 21-61 7 35 7.2 0-41 Control (n = 43) 6 37 32.8 (8.6) 20-50 5.5 0-22 analysis was conducted in four steps using STATA IC/11.0 (Stata Corp). First, the cost differences between the treatment and control groups were estimated at pre-treatment and post-treatment. All costs were extrapolated to a 12-month period. Since the cost data were assumed to be non-normally distributed, p-values were estimated using a general linear model while employing non- parametric bootstrap analysis (5,000 replications). Such analyses are considered to generate robust estimates of standard errors of the costs [25]. Secondly, we compared the counts of clinically signifi- cant improvements across the conditions. Using a Pois- son regression framework allowed us to test if the rate of favorable treatment responses in the experimental condition was higher than 1 relative to the control con- dition. The number needed treat (NNT) was computed as the inverse of the risk difference, which was obtained under a linear probability model [26]. Thirdly, the incremental cost-effectiveness ratio (ICER) was estimated. We computed pre-post differences in costs and effects. We then computed differences in costs and effects between both conditions taking the cost dif- ference over the effect difference: (C(exp) - C(ctr))/(E (exp) - E(ctr)). C is the difference of the costs of IBS and the intervention between the pre and post assessments. E refers to the treatment response between both conditions [22]. This calculation was repeated 5,000 times (for each bootstrap sample) generating a scatter of simulated ICERs across the ICER plane (see Figure 2). Fourthly, the robustness of the results was tested in four different sensitivity analyses. First, the main analysis was repeated with only the direct medical costs included, hence narrowing the economic evaluation to a health service perspective. Second, the total costs were calculated with the unemployment costs excluded. This was done because the costs due to productivity losses from unemployment are large when accumulated over a 12-month period and could (in spite of randomization) be affected by factors not associated with IBS. Third, the Page 4 of 7 Figure 2 Cost-effectiveness plane. (no figure legend). missing data were reanalyzed using linear regression imputation as implemented in STATA. This was done to test whether last observation carried forward was indeed a more conservative imputation method. Fourth, we investigated the long-term impact on the ICER using 3-month and 1-year follow-up economic data. Since the control group was crossed over to treatment there was no comparison group at the follow-up assessment. The 3-month and 1 year follow-up of the treatment group data were therefore compared with the post-treatment data of the control group, thus assuming that the con- trol group would have been unchanged if had been untreated during the follow-up periods. This was done to test whether the extrapolation to a 12-month period was a reliable analysis procedure. Lastly, since the control group was crossed over to treatment and also participated in the follow-up, the long-term costs were calculated separately for that group. Results Costs Table 2 presents the annual per capita costs of IBS at pre-treatment and post-treatment assessment for both treatment and control groups and 3-month and 1 year follow-up for the treatment group. At post treatment, we found significant cost differences regarding the gross total monetary change, Z = -2.06, P < 0.05, as well as the indirect non-medical costs, Z = -2.20, P < 0.05. Treatment efficacy The fraction of recovered participants in the interven- tion condition at post treatment was 15/42 = 0.36 and 1/43 = 0.02 in the control condition. Hence, the likeli- hood ratio for a favorable treatment response was 0.36/ 0.02 = 15.36, representing a 15-fold increase of the recovery rate in the intervention relative to the control condition (95% confidence interval 2.1 to 111.1). This