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

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24 Volume Outcome KCE reports 113 Cox regression (survival analysis) While short-term outcome (such as in-hospital mortality) can perfectly be handled with logistic regression, long-term outcome (such as 5-year survival) requires more sophisticated techniques from time to event analyses, as some outcome data might be censored. The Cox proportional hazard (PH) model is the most used regression model to deal with censored data. Specific articles Some articles were classified apart from the above categories, as they discussed specific methodological questions. One article discussed the correct correlation coefficient for volume outcome relationship studies, and three articles discussed in detail methods to adjust for clustering of data. These include the hierarchical models (also known as multilevel or mixed models) and the Generalized Estimating Equations (GEE) method. The above articles form what could be called the standard methods for analysing the volume outcome data. Thereupon, other methods, mostly econometrical from origin, were used to specifically test the causality of the relationship. Briefly explained: does the “practice makes perfect hypothesis” (the more one does something, the better one is at doing it) or does the “selective referral hypothesis” (better providers have more volume because they have better quality) explain the relationship? These two hypotheses are discussed in section 3.7.1. Methods assessing the causality are: Simultaneous equations modelling Simultaneous equations modelling is a standard econometrical method used to analyze models with endogenous variable (as volume could be if the selective referral hypothesis is true). There are two equations: one explains outcome as a function of volume, and another which explains volume as a function of outcome. More details can be found in section 3.7.2. Instrumental variables This approach is linked to the previous one, as this method specifically constructs new variables, called instruments that are used to adjust the coefficients of the relationship for the possibility of selective referral. A legitimate instrument must be conditionally unrelated with the dependent variable of interest (mortality) except through its effect on the potentially endogenous regressor of interest (volume). 84 These methods are described in section 3.7.3 Longitudinal studies In these studies, hospitals outcomes data are observed during a certain period of time, assessing what happens in the hospital over time as its volume changes. This has the advantage of keeping other characteristics of hospital and patients unaltered. 33 This ends the chapter on the classification of methods. The remainder of the chapter discusses in detail the methods presented above, and is organized as follows: 1. presenting the data 2. estimating and testing the relationship (regression models) 3. investigating the causality
KCE Reports 113 Volume Outcome 25 3.5 PRESENTING THE DATA 3.5.1 Which outcome? Outcome data can be either continuous (such as length of hospital stay), binary (such as mortality or complication at a certain time point) or as time to event (with censored follow up times). As the vast majority of the literature discusses mortality data at a specific time point, this is also the approach followed in this text, to simplify reading. When the methods differ for continuous or time to event data, this will be mentioned. 3.5.2 Which volume? While the vast majority of the literature discusses the volume of the hospital, there is a growing body of literature which focuses on the relative importance of the physician volume in contrast, or in addition, to the hospital volume. Importantly, these two volume measures have different implications. While surgeon volume is a good proxy for the surgeon experience (either accumulated or current), there is no equivalent for the hospital volume. Hospital volume might be a proxy for different processes of care, staffing, or organizational structures. If both are available, correlations between hospital volume and physician volume should be investigated in order to produce valid empirical estimates. 82 The figure below presents the conceptual framework in how the volume of a centre affect the outcome could. It shows that the outcome of a centre is a complex combination of patient selection, patient case mix (severity and co morbidities) and processes of care (choice of treatment, organization). Surgeon volume is just one of the many dimensions of the physician skills. Skills of other physicians than the surgeon can also play a role on the outcome. Figure 3.1: Conceptual Framework: How Could Volume Affect Quality? In Halm et al. 38 Urbach investigated the relationship between the hospital volume of a specific procedure and the outcome of another procedure, and found some associations. 85 Some authors also tested the relationship between previous surgeon or hospital volume (of previous years), with current outcome, aiming to test the experience at the time of surgery.
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73. Financiering van het zorgprogra
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