The Library

A statistical framework for quantifying clinical equipoise for individual cases during randomized controlled surgical trials

Tools

Parsons, Nicholas R., Kulikov, Yuri, Girling, Alan J. and Griffin, Damian R.. (2011) A statistical framework for quantifying clinical equipoise for individual cases during randomized controlled surgical trials. Trials, Vol.12 (No.1). p. 258. ISSN 1745-6215

Preview

PDF
WRAP_Parsons_1745-6215-12-258.pdf - Published Version - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (461Kb)

Official URL: http://dx.doi.org/10.1186/1745-6215-12-258

Abstract

Background
Randomised controlled trials are being increasingly used to evaluate new surgical interventions. There are a number of problematic methodological issues specific to surgical trials, the most important being identifying whether patients are eligible for recruitment into the trial. This is in part due to the diversity in practice patterns across institutions and the enormous range of available interventions that often leads to a low level of agreement between clinicians about both the value and the appropriate choice of intervention. We argue that a clinician should offer patients the option of recruitment into a trial, even if the clinician is not individually in a position of equipoise, if there is collective (clinical) equipoise amongst the wider clinical community about the effectiveness of a proposed intervention (the clinical equipoise principle). We show how this process can work using data collected from an ongoing trial of a surgical intervention.

Results
We describe a statistical framework for the assessment of uncertainty prior to patient recruitment to a clinical trial using a panel of expert clinical assessors and techniques for eliciting, pooling and modelling of expert opinions. The methodology is illustrated using example data from the UK Heel Fracture Trial. The statistical modelling provided results that were clear and simple to present to clinicians and showed how decisions regarding recruitment were influenced by both the collective opinion of the expert panel and the type of decision rule selected.

Conclusions
The statistical framework presented has potential to identify eligible patients and assist in the simplification of eligibility criteria which might encourage greater participation in clinical trials evaluating surgical interventions.

Item Type:

Journal Article

Subjects:

R Medicine > RD Surgery

Divisions:

Faculty of Medicine > Warwick Medical School

Library of Congress Subject Headings (LCSH):

Surgery -- Research, Clinical trials -- Methodology, Clinical trials -- Mathematical models

Journal or Publication Title:

Trials

Publisher:

Bio Med Central

ISSN:

1745-6215

Official Date:

13 December 2011

Dates:

Date	Event
13 December 2011	Published

Volume:

Vol.12

Number:

No.1

Page Range:

p. 258

Identification Number:

10.1186/1745-6215-12-258

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Open Access

Funder:

Arthritis Research UK, Engineering and Physical Sciences Research Council (EPSRC), Warwick Medical School

Grant number:

15964 (ARUK), GR/S29874/01 (EPSRC)

References:

1. Boutron I, Ravaud P, Nizaed R: The design and assessment of prospective
randomised, controlled trials in orthopaedic surgery. Journal of Bone and
Joint Surgery (British) 2007, 89:858-863.
2. Horton R: Surgical research or comic opera: questions, but few answers.
Lancet 1996, 347:984-985.
3. McCulloch P, Taylor I, Sasako M, Lovett B, Griffin D: Randomised trials in
surgery: problems and possible solutions. BMJ 2002, 324:1448-1451.
4. Halpern SD: Evidence-based equipoise and research responsiveness.
American Journal of Bioethics 2006, 6:1-4.
5. Freedman B: Equipoise and the ethics of clinical research. New England
Journal of Medicine 1987, 317:141-145.
6. Chard JA, Lilford RJ: The use of equipoise in clinical trials. Social Science
and Medicine 1998, 47:891-898.
7. Jaynes ET: Probability theory: the logic of science Cambridge: Cambridge
University Press; 2003.
8. Spiegelhalter DJ, Abrams KR, Myles JP: Bayesian approaches to clinical trials
and health-care evaluation New York: Wiley; 2004.
9. Kandane JB, Wolfson LJ: Experiences in elicitation. The Statistician 1998,
47:3-19.
10. Lilford R: Formal measurement of clinical uncertainty: prelude to a trial
in perinatal medicine. BMJ 1994, 308:111-112.
11. Young J, Harrison J, White G, May J, Solomon M: Developing measures of
surgeons’ equipoise to assess the feasibility of randomized controlled
trials in vascular surgery. Surgery 2004, 136:1070-1076.
12. Parmar MKB, Griffiths GO, Spieglelhalter DJ, Souhami RL, Altman DG, van
der Scheuren E: Monitoring of large randomised clinical trials: a new
approach with Bayesian methods. Lancet 2001, 358:375-381.
13. Parmar MKB, Spiegelhalter DJ, Freedman LS: The chart trials: Bayesian
design and monitoring in practice. Statistics in Medicine 1994,
13:1297-1312.
14. Spiegelhalter DJ, Freedman LS, Parmar MKB: Bayesian approaches to
randomized trials. Journal of the Royal Statistical Society, Series A 1994,
157:357-416.
15. Jøsang A: Artificial reasoning with subjective logic. In Proceedings of the
Second Australian Workshop on Commonsense Reasoning: December 1997;
Perth Edited by: Nayak A, Pagnucco M .
16. Jøsang A: A logic for uncertain probabilities. International Journal of
Uncertainty, Fuzziness and Knowledge-Based Systems 2001, 9:279-311.
17. Evans M, Hastings N, Peacock B: Beta Distribution. Statistical Distributions. 3
edition. New York: Wiley; 2000, 34-42.
18. Genest C, Zidek JV: Combining probability distributions: a critique and an
annotated bibliography. Statistical Science 1986, 1:114-135.
19. O’Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ,
Oakley JE, Rakow T: Uncertain Judgements: Eliciting Expert Probabilities
Chichester: John Wiley and Sons; 2006.
20. Johnson N, Lilford RJ, Brazier W: At what level of collective equipoise
does a clinical trial become ethical? Journal of Medical Ethics 1991,
17:30-34.
21. Efron B, Tibshirani R: An introduction to the bootstrap Boca Raton, Florida:
Chapman and Hall/CRC; 1993.
22. R Developmental Core Team: R: A language and environment for
statistical computing. R Foundation for Statistical Computing, Vienna,
Austria; 2010 [http://www.R-project.org].
23. Venables WN, Ripley BD: Modern applied statistics in s-plus. 4 edition. New
York: Springer; 2002.
24. Lilford R, Jackson J: Equipoise and the ethics of randomization. Journal of
the Royal Society of Medicine 1995, 88:552-559.
25. Torgerson D, Sibbald B: Understanding controlled trials: What is a patient
preference trial? BMJ 1998, 316:360.