Appendix 2. Description of statistical methods
Descriptive statistics for all measures of HRQOL were obtained. To identify the dimensions of HRQOL most significantly affected by severe trauma we compared the values reported by patients in the trial with corresponding population norms (i.e., population mean values) reported by the general population. HRQOL data for patients not suffering from severe trauma were not collected in the trial and the difference between the trial values and the population norms was taken as an indication of the impact of severe trauma on HRQOL. For the EQ-5D the population norms were for males and females aged 35-44 years (the mean age
[SD] of patients in the trial was 38 years [15]) in the US general population.
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For the SF-36 the 1998 general US population mean values were used 20 , while data for German patients with minor injuries was applied for the TOP (Lefering et al, Quality of Life after multiple trauma: validation and population norm of the Polytrauma Outcome (POLO)-Chart; in press).
Differences were assessed using a one sample mean comparison test (t-test). To identify predictors of poor HRQOL, all HRQOL measures were regressed against the covariates listed above. The dependent variables were: (1) EQ-5D score measured as a continuous variable in the range -0.102 to 1.000 (estimated using ordinary least squares, OLS); (2) a binary variable taking the value 1 if EQ-5D score ≥ 0.6 and 0 otherwise (based on the observed distribution of
EQ-5D scores, logistic regression); (3) ten SF-36 models based on the scores in each of the 8 dimensions plus the physical and mental summary measures, which are in the range 0 to 100
(OLS); and, (4) ten TOP models also based on the scores in each dimension, also in the range
0 to 100 (OLS). The predictors were selected using the EQ-5D score model and then applied to all other models. The predictors in the EQ-5D score model were selected using forward and backward stepwise selection techniques. The sequential addition of country, demographic variables, pre-injury socioeconomic status variables, injury type, injury mechanism, injury

severity, LOS, compliance with clinical guidelines, and the post-injury socioeconomic status variable was examined, and only significant variables were retained after each addition. The final model was then applied to 22 dependent variables. Standard errors were adjusted for hospital level clustering in all models. All models were estimated using Stata version 10
(StataCorp LP, College Station, Texas). P-values <0.10 were considered statistically significant.