Aanesthetic Agents for Day Surgery - NIHR Health Technology ...

Costs
The total cost was calculated for each of the
patients enrolled in the trial and allocated to one
of the comparators. The total cost was the sum of
all costs incurred on behalf of the patient, from
the perspective of the NHS. The variable costs
and costs associated with postdischarge resource
use were reported separately. The mean cost per
patient for each comparator was estimated as the
sum of the total costs for all patients randomised
to that intervention, divided by the number of
patients randomised. The costs incurred by the
patient, from the perspective of the patient or
parent/guardian, were reported separately.
The analysis excluded cost estimates for missing
patients (patients who did not complete
follow-up).
Incremental cost-effectiveness ratios
ICERs were calculated for the trial-based analyses
and sensitivity analyses. The effectiveness measure
for the calculation of the ICER was PONV (cost
per case of PONV avoided). Variable costs were
used in this analysis because the fixed costs
component did not differ between randomisation
arms. The interventions were ranked from highest
to lowest cost. Interventions with high rankings on
cost, which also have lower outcomes than the next
most costly comparator, were treated as inefficient
and excluded from further analysis. If the lowest
cost intervention was also associated with better
outcomes than more costly comparators, this was
treated as efficient. Incremental ratios would not
be calculated for this intervention, since its use
would lead to both net savings and greater
benefits than any other comparator.
ICERs were calculated for the remaining
interventions. Each intervention was compared to
the comparator ranked immediately below it in
terms of cost. The incremental ratios were
calculated as:
ICER = (Cost A – Cost B)/(Outcome A –
Outcome B)
Statistical analyses
The SAS ® and SPSS ® statistical software packages
were used. The objective of the statistical analysis
was to test whether there were statistically significant
differences between groups in the primary
outcome (PONV), willingness-to-pay values,
resource use and costs. Nominal data (e.g.
incidence of PONV) were mainly analysed
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Health Technology Assessment 2002; Vol. 6: No. 30
using the χ 2 test. So-called ‘exact’ tests were
used when appropriate. Regression analyses
were employed to confirm the findings from
tabular analyses and to explore fine detail
and interrelations not easily studied
through tabulation.
Differences in mean values for continuous
economic data were analysed using parametric
tests of differences in mean values (length of stay,
cost, CV and net benefit). Typically, these variables
have positively skewed distributions. 253 The main
options for statistical analysis were: standard
non-parametric methods, data transformation,
standard parametric methods, and nonparametric
bootstrapping.
Arithmetic means provide a measure of central
tendency that incorporates the full distribution of
observations. The arithmetic mean is considered to
be the most relevant measure for healthcare policy
decisions, which should be based on information
about the distribution of the costs of treating a
patient group, as well as the average cost. The
choice of statistical approach was based on the
need to calculate and test for significant differences
in the arithmetic mean in potentially
skewed data.
Non-parametric statistical tests were considered
inappropriate because they do not test differences
in arithmetic means. 253 Similarly, data transformation
to achieve approximate normality does not
result in a comparison of arithmetic means
(e.g. geometric means are derived during
log transformation).
Bootstrapping compares arithmetic means,
while avoiding distributional assumptions. This
technique is most useful where the sample size
is small to medium. Work carried out to compare
the performance of bootstrapping with parametric
t-tests has shown the t-test to be “remarkably robust
to non-normality”. 253 This robustness requires the
sample size to be large enough for the central
limit theorem to act sufficiently, or for the sample
size and skewness to be similar in the groups
under comparison. 254
The t-test has been shown to be robust and give
similar results to non-parametric bootstrapping
with sample sizes of 148, where there was “severe
non-normality”. 253 The sample sizes in this study
were much larger. It is suggested that in trials
like this study, which are large enough to
influence healthcare policy, standard t-test based
approaches will be robust and give results very
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