Post-hoc Analyses in Clinical Trials, A Case for Logistic Regression Analysis

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
Chapter

Abstract

Multiple variables methods are used to adjust asymmetries in the patient characteristics in a trial (see page 171 for a discussion of the difference between multivariate and multiple variables methods). It can also be used for a subsequent purpose. In many trials simple primary hypotheses in terms of efficacy and safety expectations, are tested through their respective outcome variables as described in the protocol. However, sometimes it is decided already at the design stage that post hoc analyses will be performed for the purpose of testing secondary hypotheses. For example, suppose we first want to know whether a novel beta-blocker is better than a standard beta-blocker, and second, if so, whether this better effect is due to a vasodilatory property of the novel compound. The first hypothesis is assessed in the primary analysis. For the second hypothesis, we can simply adjust the two treatment groups for difference in vasodilation by multiple regression analysis and see whether differences in treatment effects otherwise are affected by this procedure. However, with small data power is lost by such procedure. More power is provided by the following approach. We could assign all of the patients to two new groups: patients who actually have improvement in the primary outcome variable and those who have not, irrespective of the type of beta-blocker. We, then, can perform a regression analysis of the two new groups trying to find independent determinants of this improvement. If the dependent determinant is binary, which is generally so, our choice of test is logistic regression analysis. Testing the second hypothesis is, of course, of lower validity than testing the first one, because it is post-hoc and makes use of a regression analysis which does not differentiate between causal relationships and relationships due to an unknown common factor.

Keywords

Logistic Regression Peripheral Vascular Resistance Rate Pressure Product Independent Determinant Primary Outcome Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Cleophas TJ, Remmert HP, Kauw FH (1996) Celiprolol versus propranolol in unstable angina pectoris. Clin Pharmacol Ther 45:476–473Google Scholar
  2. Van der Vring AF, Cleophas TJ, Zwinderman AH et al (1999) Different classes of calcium channel blockers in addition beta-blockers for exercise induced angina pectoris. Br J Clin Pharmacol 50:545–560Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Ton J. Cleophas
    • 1
    • 2
  • Aeilko H. Zwinderman
    • 1
    • 3
  1. 1.Applied to Clinical TrialsEuropean Interuniversity College of Pharmaceutical MedicineLyonFrance
  2. 2.Department of MedicineAlbert Schweitzer HospitalDordrechtNetherlands
  3. 3.Department of Biostatistics and EpidemiologyAcademic Medical CenterAmsterdamNetherlands

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