Literature DB >> 16794903

Clinical trial results applied to management of the individual cancer patient.

Ismail Jatoi1, Michael A Proschan.   

Abstract

The application of clinical trial results to the management of the individual cancer patient is not always straightforward. The results of a clinical trial indicate the "average" effect of an intervention, often expressed in terms of an absolute risk reduction, which is an estimate of the likelihood of benefit for a particular patient. However, within any clinical trial, there might be differences between groups of patients in underlying pathology, genetics, or biology, and some patients might benefit more from a new treatment than others. Thus, within a clinical trial, it might also be useful to group together patients with similar characteristics, and test for subgroup interaction. The test for interaction will indicate whether the magnitude of benefit differs from one prognostic subgroup to the next (a quantitative interaction). Much less common are qualitative interactions, in which a new treatment is beneficial in one subgroup but harmful in another. If the test for subgroup interaction is significant, then the effects of treatment may indeed differ between subgroups of patients, but this should be confirmed in other trials before a treatment is implemented in clinical practice.

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Year:  2006        PMID: 16794903     DOI: 10.1007/s00268-006-0073-x

Source DB:  PubMed          Journal:  World J Surg        ISSN: 0364-2313            Impact factor:   3.352


  19 in total

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Authors:  D Mant
Journal:  Lancet       Date:  1999-02-27       Impact factor: 79.321

Review 2.  'Real world' pragmatic clinical trials: what are they and what do they tell us?

Authors:  Peter J Helms
Journal:  Pediatr Allergy Immunol       Date:  2002-02       Impact factor: 6.377

Review 3.  Individual response to treatment: is it a valid assumption?

Authors:  Stephen Senn
Journal:  BMJ       Date:  2004-10-23

4.  Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

Authors:  Peter M Rothwell
Journal:  Lancet       Date:  2005 Jan 8-14       Impact factor: 79.321

5.  Beyond subgroup analysis: improving the clinical interpretation of treatment effects in stroke research.

Authors:  Mei Lu; Patrick D Lyden; Thomas G Brott; Scott Hamilton; Joseph P Broderick; James C Grotta
Journal:  J Neurosci Methods       Date:  2004-12-08       Impact factor: 2.390

6.  Trials: the next 50 years. Large scale randomised evidence of moderate benefits.

Authors:  R Peto; C Baigent
Journal:  BMJ       Date:  1998-10-31

7.  Subgroup analysis and other (mis)uses of baseline data in clinical trials.

Authors:  S F Assmann; S J Pocock; L E Enos; L E Kasten
Journal:  Lancet       Date:  2000-03-25       Impact factor: 79.321

8.  Tamoxifen for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group.

Authors: 
Journal:  Lancet       Date:  1998-05-16       Impact factor: 79.321

Review 9.  Applying overviews and meta-analyses at the bedside.

Authors:  D L Sackett
Journal:  J Clin Epidemiol       Date:  1995-01       Impact factor: 6.437

10.  The number needed to treat: a clinically useful measure of treatment effect.

Authors:  R J Cook; D L Sackett
Journal:  BMJ       Date:  1995-02-18
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