You are here

Article Text

Article menu
Download PDFPDF
EBM Learning
General medicine
Inclusion and exclusion criteria and the problem of describing homogeneity of study populations in clinical trials
  1. Franz Porzsolt1,2,
  2. Felicitas Wiedemann1,3,
  3. Susanne Isabel Becker1,4,
  4. C J Rhoads5
  1. 1 Institute of Clinical Economics (ICE) e.V., Ulm, Germany
  2. 2 Health Care Research at the Department of General and Visceral Surgery, University Hospital Ulm, Ulm, Germany
  3. 3 Klinik für Allgemein- und Viszeralchirurgie, Diakonie-Klinikum Stuttgart, Stuttgart, Germany
  4. 4 Klinik für Seelische Gesundheit, Karl Hansen Klinik, Bad Lippspringe, Germany
  5. 5 College of Business, Kutztown University, Kutztown, Pennsylvania, USA
  1. Correspondence to Dr Franz Porzsolt, Health Care Research at the Dept General and Visceral Surgery, University Hospital Ulm, Ulm 89071, Germany; f.porzsolt{at}gmx.de

https://doi.org/10.1136/bmjebm-2018-111115

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    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.

    Method

    This study builds on on the data of research done …

    View Full Text

    Footnotes

    • Contributors All authors contributed substantially to the paper. FW and FP were primarily responsible for the acquisition of the data, but all authors contributed substantially into the analysis and review of the data, the development of the report and editing of the paper for submission.

    • Competing interests None declared.

    • Ethics approval This study includes only published information that cannot be related to individual subjects.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement The results on the remaining six criteria are available (in German language) through FW and FP. We are planning additional publications on these papers.