Het volume van chirurgische ingrepen en de impact ervan op ... - KCE

KCE Reports 113 Volume Outcome 29
3.5.4.2 Presenting the data in a table: choice of the volume cut off
Some studies divide the patients into groups of equal volume (for example in tertiles,
quartiles or quintiles) and then compare mortality between these groups. The advantage
from a statistical perspective is that it ensures that the test comparing the groups has
maximal power. But this approach has also drawbacks: 30 there is a great variability in
volume between the groups (depending on the distribution of patients within hospitals),
potentially ending in meaningless cut off values.
Other studies divide the hospitals into groups of equal volume, based on percentiles or
on predefined cut off values (international thresholds). This approach has the advantage
of leading to results easier to interpret, but it implicates that low and high volumes
should be defined before the analysis. Of course it would be tempting to deliberately
select volume threshold after the analysis of data, in order to maximize the difference
between the groups. But, this approach is to be avoided, as it makes it impossible to
interpret the claimed level of significance (the alpha level of the test). 92 For the same
reason, the approach taken by some authors to find the best threshold for volume by
testing all different cut off criteria is not valid. 93 It can only serve to generate hypotheses
which need to be confirmed on another set of data.
Once data have been presented graphically and summarized in a table format, the next
step is to estimate and test the importance of the association.
3.6 ESTIMATING AND TESTING THE VOLUME OUTCOME
RELATIONSHIP
Regression models, with outcome as the dependent variable and (a function of) volume
as the independent variable, can be used to test the association between the 2 variables.
Logistic regression (for binary outcome data) and linear regression (for continuous
outcome data) can be used to model individual or aggregated data, the former allowing
more flexibility in the choice of the model and in the adjustment for severity of patients.
Hierarchical models (synonyms are multilevel model and mixed models) can also take
into account the hierarchical nature of health care data (patients nested by physician,
and physicians nested by health care provider). These topics are discussed in detail
hereafter.
3.6.1.1 Simple regression models
A formal test of the association between volume and outcome can simply be obtained
by a logistic regression of the outcome for a patient in a centre on the volume or log
volume of that centre.
These models are referred to as conventional logistic regression models, meaning that
they do not really account for the hierarchical structure in the data. 94 They easily allow
for adjustment of other patient covariates (see section case mix adjustment 3.6.1.2).
When the log of volume is used as explicative factor, the coefficient β has the following
attractive interpretation, based on a relative change in volume: a small percentage rise
x% in sample size leads approximately to a β times x percent change in the odds of
death.
When the absolute value of volume is used, the interpretation of the odds ratio (OR) is
based on the absolute change in volume unit. For each additional unit of volume, the
estimated risk for each patient (expressed as the odds of event) is reduced by
-100 (1- OR) %. 95% CI and p-values can be derived from all models.
3.6.1.2 The need to adjust for patient severity (Case Mix)
A serious problem in the analysis of the volume outcome relationship is the potential
confounding effect of differences in patient severity of illness, as individual factors
strongly influence outcomes. The crucial question is whether more (or less) severely ill
patients are consistently being treated in high (or low) volume hospitals.
Determining whether the volume outcome relationship is the result of case mix
differences would be possible if large numbers of patients were randomly assigned or
referred to institutions with varying volume levels.