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The description of results in clinical trials is an unsolved problem.1 The low quality of some clinical trials and the low output of useful data for healthcare decisions is a problem for clinicians and patients alike. Statisticians doing systematic reviews or meta-analysis try to solve this problem by establishing lower p-value thresholds (for example, changing from 0.05 to 0.005). That results in the acceptance of fewer, larger and more carefully designed studies in order to be considered to have sufficient power.2 The problem is that good useful information might get suppressed using stricter p-value thresholds. This is an especially difficult problem in quality of life (QoL) studies which, by their very nature, are smaller and less powerful.
QoL researchers believe that the answer to poor quality is to review more studies that describe meaningful positive outcomes that contribute to the improvement of healthcare.3 What is often missing is a general discussion about the selection criteria.
Choosing selection criteria is not easy. On one hand, wide inclusion criteria should be chosen in order to demonstrate safety and effectiveness for a large group of patients. On the other hand, there are many ethical and scientific reasons to define exclusion criteria.4 The resulting subjective selection of inclusion and exclusion criteria should be a balance of these two considerations.
In this report, we analyse the information provided by the description of inclusion and exclusion criteria. We demonstrate that valuable information on the investigated study population can be provided by a complete and precise description of the inclusion and exclusion criteria that is not present in all publications. Furthermore, we propose that researchers can derive important information on the homogeneity/heterogeneity of different study populations from completely reported inclusion and exclusion criteria.
This study builds on on the data of research done …
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