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

KCE Reports 113 Volume Outcome 31
Figure 3.6: An example of 3 level structure (level 1 patient, level 2 provider,
level 3 institution)
Figure 3.7: An example of a cross classified 3 level structure (level 1 patient,
level 2 provider, level 3 institution)
In a typical volume-outcome analysis, the outcome is measured at the level of the
individual patient. To account for differences between patients which also influence the
outcome of interest, the variation in these factors must be taken into account in the
analysis. The usual method is logistic regression analysis, with institutions characteristics
attributed to individual patients, and inference of institutions is estimated in a single level
multivariable regression model taking other potential confounders into account.
A well known and well described problem with this approach is that it ignores the
clustering of patients within hospitals. One of the hypotheses of conventional regression
is that observations are independent of each other. This can be violated when data are
clustered, because they share other characteristics than the volume and therefore the
amount of information present in the data is less than in independent data. The
consequence of using conventional logistic regression that does not take into account
the clustering of data is that it tends to underestimate the standard error of the
regression coefficient, and therefore overestimates statistical significance of apparent
effects (standard errors are too small). In other words, an apparent statistically
significant relationship using a conventional model might turn out to be non significant
when clustering of data is accounted for. This has been extensively described by Urbach
94, 96, 97
and Panageas among many others.