gms | German Medical Science

10. Deutscher Kongress für Versorgungsforschung, 18. GAA-Jahrestagung

Deutsches Netzwerk Versorgungsforschung e. V.
Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e. V.

20.-22.10.2011, Köln

How to generate evidence for the clinical benefit of a diagnostic method

Meeting Abstract

  • corresponding author presenting/speaker Werner Vach - Clinical Epidemiology, University Medical Center Freiburg, Freiburg, Germany
  • author Poul Flemming Hoilund-Carlsen - Dept. of Nuclear Medicine, Odense University Hospital, Odense, Denmark
  • author Oke Gerke - Dept. of Nuclear Medicine, Odense University Hospital, Odense, Denmark
  • author Wolfgang Weber - Dept. of Nuclear Medicine, University Medical Center Freiburg, Freiburg, Germany

10. Deutscher Kongress für Versorgungsforschung. 18. GAA-Jahrestagung. Köln, 20.-22.10.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11dkvf117

DOI: 10.3205/11dkvf117, URN: urn:nbn:de:0183-11dkvf1171

Published: October 12, 2011

© 2011 Vach et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

Background: Today diagnostic methods like PET/CT have to demonstrate not only their diagnostic accuracy, but also their clinical benefit [1]. However, there is a lack of consensus about how to approach this aim. [2].

Materials and methods: We review some basic approaches to demonstrate the clinical benefit of a new diagnostic method compared to a current standard procedure: accuracy studies, decision modeling, ungated and gated RCTs, management studies and clinical registries. We discuss the basic problems, possibilities and limitations using some typical diagnostic scenarios:

Replacement of a current invasive procedure, improved accuracy of initial diagnosis, improved accuracy of staging – curative vs. palliative treatment and radiation vs. chemotherapy – response evaluation, and acceleration of decisions.

Results: Decision modeling suffers a first sight from the basic limitation that we need to assess the expected benefit in those subjects for whom the result of the new procedure differs from the result of the current standard. Such patients are rarely the exact population of a study, hence we are forced to work with analogies and generalizations often only allowing to determine some bonds on the benefit. On the other side, an improved diagnostic accuracy implies typically many more changes in the correct than in the incorrect direction, and hence even with conservative bounds we can often still demonstrate a benefit. RCTs can avoid these problems and can additional take into account intended and unintended effects not directly related to improved diagnostic accuracy. However, as typically only a fraction of all patients will experience a change in the diagnostic decision, the impact on long term patient related outcomes is often small and hence these studies require large sample sizes.

Conclusions: We propose the following simple guideline for evaluation of the clinical benefit of a new diagnostic method: First it should be clarified whether there is a direct benefit from applying the new method, or an indirect benefit due to improved diagnosis implying better treatment and management decisions. In the first case, the second step is to demonstrate non-inferiority with respect to diagnostic accuracy. In the latter case, the second step should be to combine the available evidence on accuracy and on expected benefits due to improved management and treatment in a decision modeling to assess the expected overall benefit. Only if this does not allow definite conclusions, RCTs can and should be planned.


References

1.
Schuenemann HJ, Oxman AD, Brozek J, Glasziou P, Bossuyt P, Chang S, Muti P, Jaeschke R, Guyatt GH. GRADE: assessing the quality of evidence for diagnostic recommendations. Annals of Internal Medicine. 2008;149(12):JC6-2.
2.
Tunis SR, Benner J, McClellan M. Comparative effectiveness research: Policy context, methods development and research infrastructure. Statistics in Medicine. 2010;2(19):1963-76.