Statistical analysis

Associations between each explanatory variable and each outcome variable (according to the stated hypotheses) were assessed in univariate (one-way) analyses.

mous variables was calculated using either the pooled or atterthwaite methods, depending on the probability of the variances being

lame,

e

ssociations between PTSD, a dichotomous outcome variable, and

he

szel). Continuous outcomes (PCS, MCS, neck pain, back pain, and PTSD) and dichotomous explanatory variables (e.g., sex, claim made, use of a lawyer) were assessed by comparing means in the two groups, using a t test. The t test for dichoto

S

equal. For explanatory variables with more than two categories (e.g., b income, education and chronic illnesses) one-way analysis of variance (ANOVA) was used to assess differences in means (F test for overall association). Association with continuous variables (e.g., age, time sinc injury) were assessed using the Pearson correlation coefficient.

A

continuous variables were assessed by comparing means (using a t test). T association with other dichotomous variables was assessed using the chi- square test. The association with categorical variables was assessed using the chi-square tests for overall association and for trend (Mantel-Haen

Associations between the ordered categorical outcome (patient satisfaction) and continuous variables was assessed using ANOVA (F test for overall

association), and the chi-square tests for overall association and for trend (Mantel-Haenszel) was used to assess the association with dichotomous and categorical explanatory variables.

Any variable with a significance level of 0.25 or lower on univariate an was included in a multiple regression equation, separately for each outcome.

alysis

formed to find the model which best xplained the changes in the outcome. Variables were removed sequentially,

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nce

e since injury and ISS), studentised residuals were also plotted gainst predicted values, and the histogram, boxplot and normal probability

all

ple size was large and there were no fluential points.414

413

Backward elimination was per e

removing the variable with the least significance at each step. Variables with significance levels of 0.05 or less were retained in the final model. Interaction terms were introduced into the final model and retained if their significanc level was less than or equal to 0.01.

For multiple linear regression, the assumptions of linearity and equal varia about the mean were tested by noting observations whose Cook’s distance was greater than 1 (indicating undue influence on the regression model), studentised residuals were plotted against the continuous variables in the model (tim

a

plot for the distribution of residuals was examined. The assumption of normality was tested using the Proc Univariate procedure in SAS. A sm negative skew in the distribution of the residuals for neck pain and back pain models was accepted as the sam

Due the importance of a linear association between continuous predictor variables and dichotomous outcomes in logistic regression, the continu variables were each divided into five even groups (quintiles) in order of ra using the Proc Rank statement in SAS, and analysed as categorical variabl

ous nk,

es.

o ”. The referent group was “No claim made”, and o dummy variables were created: “Claim made – settled” and “Claim made not settled”.

As ISS was positively skewed, an alternative variable was created by

logarithmic transformation of the ISS value. This new value was shown to be less positively skewed (by histogram, box plot, and stem-and-leaf plot) and was also tested against the outcomes in the analysis. In the univariate analyses, however, these two variables showed similar associations. Therefore, ISS was kept as a continuous variable without transformation.

The distribution of the stay in ICU (measured in days) was highly positively skewed, with 39.4% of subjects not admitted to ICU, and most admissions being for only 1 – 4 days. Although ICU stay was retained as a continuous variable due to the large sample size, ICU stay was also tested as a

dichotomous variable (admitted / not admitted). ICU was also recoded as an ordered categorical variable in six groups (0, 1, 2, 3, 4, 5+ days) and tested against each outcome variable. The categorical variable for ICU was not The variables “claim made” and “claim settled” were combined to allow use of data from all participants in one model. These variables were combined t make a 3-part variable, “Claim

tw –

remotely associated (p<0.25) with any of the outcome variables except low ack pain (F value = 1.70, DF = 5, 349, p = 0.13), however for low back pain,

trend, with the highest mean scores for low back pain ccurring in those whose ICU stay was 0, 3 or 4 days, and the lowest means

CU

ly herefore, an alternative dichotomous variable was created y using a cut-off score of six for “significant” pain. This was based on a

oth outcomes gave similar final models, although with higher ignificance levels for the continuous outcomes. Although the continuous

l

a more easily interpretable outcome, patient satisfaction was

ichotomised by combining the first two responses as “satisfied” and the last b

there was no observed o

scores in those whose ICU stay was 1 or 2 days. Consequently, the information in the tables regarding the analysis of ICU stay is restricted to I as a dichotomous or continuous variable.

The outcome scores for neck pain and back pain (range 2 to 11) were high positively skewed. T

b

reasonably even distribution of responses, and required reasonably high responses. B

s

outcome scores were not normally distributed, they were used in the fina model as these models were supported by the alternative (logistic analysis), and because the assumptions for multiple regression were not violated (see above).

To simplify the statistical analysis for the multivariate modelling, and to provide

d

The effects of time to settlement and time since settlement were not explored the main analysis as data were only available for a small subset of

participants (those whose cases had settled). If significant on univariate analysis, the effect of these variables was explored s.

he units of measurement of the exposure and outcome variables are given in ppendix 8. All of the statistical calculations were performed using SAS

ersion 8.2 (Cary, NC, USA). in

in a separate analysi

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3.3Results