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

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22 Volume Outcome KCE reports 113 • selective referral The last step (and final search algorithm) was to combine each of these MESH terms (or text) to the search strategy from chapter 2 (without the selection of reviews and meta analysis), combined with the "Statistics"[Mesh] term, as papers describing a statistical method are flagged with that term. The whole search strategy is given in Appendix 11 (see Supplement). From the 1 318 articles identified with that strategy, a first selection was based on the title, to select only those articles pertinent to the volume outcome relationship. A total of 145 articles were selected. After that, all abstracts of those articles were reviewed, and studies lacking the description of the methodology used were excluded. With the review of references and results from searching the grey literature, 10 other studies were added. A total of 98 studies were thus analyzed for their methodology. The graphical flow of selection of articles is presented in Appendix 12. Two books on the volume outcome relationship were also identified i.e. by Luft et al. 82, 83 and by Seider. A summary of the sources for the search is presented below: a. 8 specific methodological journals (using text searches) to obtain key words b. Medline (using MESH terms and using text fields) c. The grey literature (Google and Google scholar) for unpublished papers d. All references from articles selected above e. Amazon (for books) 3.3.2 Classification of methods All 98 articles were subsequently classified with regard to two criteria: the design of the study (cross sectional, longitudinal) and the analysis method (regression, choice of covariate, etc.). The oversimplification of such classification scheme compared to a full taxonomy is acknowledged, and should be regarded in light of the aim of the literature search (identify the methods). 3.4 RESULTS: CLASSIFICATION OF METHODS The classification of methods is based on the exercise undertaken by Luft et al. in 1990, where the authors selected 25 major articles on the volume outcome relationship and classified them into 6 categories, based on the methodology used. 82 The classification by Luft has been extended to include new methods published after 1990 (mainly methods based on econometrics). The different statistical methods used to analyze the volume-outcome relationship are presented in Appendix 13 (see Supplement), with the number of corresponding articles selected from the search described in section 3.3. It should be noted that these methods are not exclusive: a typical analysis starts with a graphical description of the data, then estimates and tests the relationship in a regression model, and, if a relationship is found, applies specific methods to investigate the causality of the relationship. Again, as mentioned above, articles were categorized based on their main analysis, or on their main methodological emphasis. Appendix 13 includes: • The design of the study: the majority of the studies were based on cross sectional designs (93 articles), only five studies were based on longitudinal data • The statistical methods used: 1. Graphical presentation, funnel plot (1 article) 2. Simple correlation coefficient (1 article) 3. Group by volume and then compare outcomes, with risk adjustments (17 articles)
KCE Reports 113 Volume Outcome 23 4. Regression methods a. Logistic regression, volume is categorical (42 articles) b. Logistic regression, volume is continuous (8 articles) c. Cox regression, volume is categorical (9 article) d. Poisson regression (2 articles) e. Methods specific to hierarchical nature of data (3 articles) 5. Specific to causality: Simultaneous equations modelling (2 articles) 6. Specific to causality: Instrumental variables (5 articles) 3.4.1 Choice of design: cross sectional or longitudinal The choice of the design has a direct influence on the conclusions that can be drawn from the study. The relation between volume and outcome merely represents an association. Although it is tempting to interpret it as a causal relationship, standard problems in epidemiology arise. For instance, it is not legitimate to say that “as volume increases, mortality falls”, and conclude that increasing volume in a hospital will improve outcome. Strictly speaking, if data are taken from a cross section of hospitals observed at a specific time, no conclusions can be drawn on the effect of increasing or decreasing the volume of that hospital on outcome. 82 On the other hand, a design based on the history of mortality at a given hospital, allowing studying the effects of the change of volume on outcome (longitudinal designs) in that hospital, does allow drawing that conclusion. Nevertheless, it is not surprising that the majority of studies are based on cross sectional data (see Appendix 13). The longitudinal design requires data on the long term (as changes in volume are not expected to occur in a few years time) and also demands more sophisticated statistical analyses. 3.4.2 Description of statistical methods presented in Appendix 13 One interesting article presents a new graphical tool named funnel plot, originally used in quality control process, which allows going beyond the traditional scatter plot (outcome versus volume). This is presented in section 3.5.4.1. Group by volume and compare outcomes, with risk adjustments The authors from these articles group hospitals by their patient volume, and then compare outcomes across the different categories. The authors also take into account case mix differences across volume categories through some type of risk adjustment. Usually case mix adjustment is performed via indirect standardization (thus comparing observed versus expected number of outcomes). The vast majority of the articles used regression methods, which are detailed below: Logistic regression, volume is categorized These regression methods involve the use of patient as the unit of observation, although the regression models can also be applied on aggregated data per hospital. Logistic regression was chosen because of the dichotomous nature of the outcome. Volume categories are usually based on the distribution of volume across hospitals (tertiles, quartiles, quintiles) or on other cut off criteria. This is by far the most preferred method of analysis, as shown by the frequencies in Appendix 13. Logistic regression, volume is continuous Some authors have performed logistic regression and considered volume as a continuous variable (which it is). Volume or log of volume has been used.
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