Abstract
Multiple clinical trials that investigate the same intervention often use different entry criteria to enroll patients into the trials. This variation in entry criteria might lead to the selection of patients with different characteristics. If these characteristics are related to the effect measure (e.g. baseline mortality in a trial investigating mortality reduction) the results of the trials cannot be compared straightforwardly. Therefore it is important to investigate whether the use of different entry criteria leads to the selection of patients with different characteristics. To study differences in the characteristics of the study populations, one could use the data from the control groups of the trials. However, differences in characteristics of these study populations can be caused by differences in entry criteria as well as by clinical diversity. Based on the data from the trials these two causes cannot be distinguished. We propose a method to investigate the influence of differences in entry criteria on the characteristics of study populations. The method corrects for the effects of clinical diversity by simulating the patient selection process on an independent registry database. The method has been successfully applied in the area of severe sepsis.
| Original language | English |
|---|---|
| Title of host publication | Connecting Medical Informatics and Bio-Informatics - Proceedings of MIE 2005: The 19th International Congress of the European Federation for Medical Informatics |
| Pages | 1004-1009 |
| Number of pages | 6 |
| Volume | 116 |
| Publication status | Published - 2005 |
| Event | 19th International Congress of the European Federation for Medical Informatics, MIE 2005 - Geneva, Switzerland Duration: 28 Aug 2005 → 1 Sept 2005 |
Conference
| Conference | 19th International Congress of the European Federation for Medical Informatics, MIE 2005 |
|---|---|
| Country/Territory | Switzerland |
| City | Geneva |
| Period | 28/08/05 → 1/09/05 |
Keywords
- Entry criteria
- Heterogeneity
- Meta analysis
- Registry
- Severe sepsis
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