68 Table 2 Differences between matched pairs Item P values and odds ratios (OR) with 95% confidence intervals (CI) for differences between the two groups in the 24 items, based on a Generalized Estimating Equations analysis for matched pairs (* if p<0.05). There is no d8 in this paediatric version. As an illustration, we added in the last column the percentages of individuals out of the trauma group (n=133) that had a ‘moderate to very severe’ score for that item or a score of 3 and more in case of the four ‘burden’ items B1a: emotional B1b: other mental B2a: pain B2b: other sensations B3: speech and voice B4: cardiorespiratory B5: intestinal, metabolic and horm. B6: urogenital and reproduction B7: locomotor system B8: skin D1: learning D2: tasks and demands D3: communication D4: mobility D5: self-care D6: domestic life D7: social relations D9: societal life S: structural F: family PB: physical burden MB: mental burden SB: social burden TB: total burden O.R. (95% CI) 4.03 (1.57; 10.38) 2.77 (0.93; 8.27) 4.05 (0.48; 34.3) 1.46 (0.52; 4.10) 2.52 (0.49; 12.96) 0.96 (0.35; 2.63) 3.27 (1.01; 10.56) 0.49 (0.09; 2.78) 17.38 (2.54; 118.91) 0.99 (0.20; 4.78) 3.07 (0.91; 10.39) 6.74 (1.68; 27.08) 1.52 (0.41; 5.66) 14.42 (3.25; 63.92) 9.03 (1.99; 41.04) 2.58 (0.48; 13.79) 6.93 (0.94; 51.12) 23.57 (3.44;161.76) 1.52 (0.28; 8.17) 4.72 (1.40; 15.94) 13.52 (1.89; 96.85) 6.19 (0.86; 44.60) 11.34 (1.46; 88.10) 3.77 (1.09; 12.99) p value 0.004* 0.067 0.200 0.468 0.268 0.937 0.047* 0.423 0.004* 0.985 0.071 0.007* 0.530 <0.001* 0.004* 0.268 0.057 0.001* 0.624 0.012* 0.010* 0.007* 0.020* 0.036* Eur J Pediatr (2011) 170:65–73 Percent of trauma cases with important problems 18.8 28.6 9.8 20.3 6.8 14.3 15.8 3 21.1 5.3 12.8 19.5 4.5 18 12 3.8 15 15 12.8 24.8 21.1 15.8 19.5 20.3 Differences between matched groups in, e.g. medical consumption or individual codes were further evaluated using Liddell’s exact test [19]. Second, to evaluate the reproducibility of our findings, a double evaluation was made in 39 children: in 11 ‘control’ children, IROS was scored simultaneously by both parents; in 28 ‘trauma’ children, both evaluations were made by the same proxy (97% mother) within a 2–3-week interval. We also obtained evaluations from both child and parent in 113 adolescents (84 controls, 29 trauma cases). We tested for marginal homogeneity and agreement between both raters, using the powerful Bhapkar test [3]. Third, we investigated the characteristics of the IROS items by means of polytomous Item Response Theory (IRT) models [31]. IRT provides a powerful class of models that can be used to identify items that are informative for specific levels of health, to evaluate the ability of an instrument to measure health and most importantly to derive a score measuring the health status of a child based on his/her responses. For our analysis, we used Samejima’s graded response model [31]. We plotted item and test information curves that determine how accurate each level of health status is being estimated by each item and by the whole set of items. We then obtained an estimate for the level of health (burden) of each individual (i.e. for each IROS response pattern): the factor score z. For interpretability reasons, these factor scores were rescaled in [0,1] using the formula exp(z) / (1 + exp(z)), where 0.5 represents the median burden and lower values, better health (less burden). Finally, based on these factor scores, we investigated the effect of collected covariates (e.g. patient characteristics, ISS, diagnosis) on the eventual patient health status by means of simple linear regression and corresponding F test. Since the final purpose is to describe health in all children irrespective of their level of illness, control and trauma cases were analysed together without considering differential item functioning. Importantly, we made in our IRT analysis a distinction between items that are treated as effect indicators (D 1–9, G and burden ‘sum’ scores) and items that are more likely causal indicators (B1–B8, S) [10, 24, 33]. In clinical trial data, a subset of items may indeed rather act as causal indicators, i.e. variables that directly affect the underlying latent variable ‘health’ and not the other way round (e.g. pain will give ‘bad’ health but not all patients with ‘bad’ health will have pain). All results were estimated with the statistical environment R (v2.8-1) and specifically, the ltm package [29]. Additional functions were written to fit the model that accounts for the effect of