Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-19T02:20:34.724Z Has data issue: false hasContentIssue false
  • English
  • Français

Cluster randomized trials: Another problem for cost-effectiveness ratios

Published online by Cambridge University Press:  04 August 2005

Terry N. Flynn  and
Tim J. Peters
Terry N. Flynn
Affiliation:
University of Bristol
Tim J. Peters
Affiliation:
University of Bristol
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: This work has investigated under what conditions cost-effectiveness data from a cluster randomized trial (CRT) are suitable for analysis using a cluster-adjusted nonparametric bootstrap. The bootstrap's main advantages are in dealing with skewed data and its ability to take correlations between costs and effects into account. However, there are known theoretical problems with a commonly used cluster bootstrap procedure, and the practical implications of these require investigation.

Methods: Simulations were used to estimate the coverage of confidence intervals around incremental cost-effectiveness ratios from CRTs using two bootstrap methods.

Results: The bootstrap gave excessively narrow confidence intervals, but there was evidence to suggest that, when the number of clusters per treatment arm exceeded 24, it might give acceptable results. The method that resampled individuals as well as clusters did not perform well when cost and effectiveness data were correlated.

Conclusions: If economic data from such trials are to be analyzed adequately, then there is a need for further investigations of more complex bootstrap procedures. Similarly, further research is required on methods such as the net benefit approach.

Keywords

Randomized controlled trialsClustered dataHealth services researchEconomics
Type
RESEARCH REPORTS
Information
International Journal of Technology Assessment in Health Care , Volume 21 , Issue 3 , July 2005 , pp. 403 - 409
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Briggs AH, Mooney CZ, Wonderling DE. 1999 Constructing confidence intervals for cost-effectiveness ratios: An evaluation of parametric and non-parametric techniques using Monte Carlo simulation. Stat Med. 18: 3245– 3262.Google Scholar
Briggs AH, Wonderling DE, Mooney CZ. 1997 Pulling cost-effectiveness analysis up by its bootstraps: A non-parametric approach to confidence interval estimation. Health Econ. 6: 327– 340.Google Scholar
Campbell M, Torgerson D. 1997; Confidence intervals for cost-effectiveness ratios: The use of ‘bootstrapping.’ J Health Serv Res Policy. 2: 253– 255.Google Scholar
Campbell MK, Mollison J, Grimshaw JM. 2001 Cluster trials in implementation research: Estimation of intracluster correlation coefficients and sample size. Stat Med. 20: 391– 399.Google Scholar
Centre for Multilevel Modelling. 2000. MLwiN software package. (1.10). London: Centre for Multilevel Modelling;
Chaudhary MA, Stearns SC. 1996 Estimating confidence intervals for cost-effectiveness ratios: An example from a randomized trial. Stat Med. 15: 1447– 1458.Google Scholar
Davison AC, Hinkley DV. 1997. Bootstrap methods and their applications. Cambridge: Cambridge University Press;
Donner A, Klar N. 2000. Design and analysis of cluster randomization trials in health research. London: Arnold;
Efron B. 1979 Computers and the theory of statistics: Thinking the unthinkable. SIAM Rev. 21: 460– 480.Google Scholar
Efron B, Tibshirani R. 1986 Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci. 1: 54– 77.Google Scholar
Flynn TN. 2002. Design and analysis of randomised controlled trials: Economic aspects of cluster randomisation. PhD thesis. University of Bristol;
Flynn TN, Peters TJ. 2005 Conceptual issues in the analysis of economic data from cluster randomised trials. J Health Serv Res Policy. 10: 97– 102.Google Scholar
Flynn TN, Peters TJ. 2004 Use of the bootstrap in analysing cost data from cluster randomised trials: Some simulation results. BMC Health Serv Res. 4: 33.Google Scholar
Flynn TN, Whitley E, Peters TJ. 2002 Recruitment strategies in a cluster randomized trial—cost implications. Stat Med. 21: 397– 405.Google Scholar
Hoch JS, Briggs AH, Willan AR. 2002 Something old, something new, something borrowed, something blue: A framework for the marriage of health econometrics and cost-effectiveness analysis. Health Econ. 11: 415– 430.Google Scholar
Polsky D, Glick HA, Willke RJ, Schulman K. 1997 Confidence intervals for cost-effectiveness ratios: A comparison of four methods. Health Econ. 6: 243– 252.Google Scholar
Rao JNK, Wu CFJ. 1988 Resampling inference with complex survey data. J Am Stat Assoc. 83: 231– 241.Google Scholar
StataCorp. 2003. Stata statistical software: Release 8.0. College Station, TX: Stata Corporation;
Ukoumunne OC, Gulliford MC, Chinn S, Sterne JAC, Burney PGJ. 1999 Methods for evaluating area-wide and organisation-based interventions in health and health care: A systematic review. Health Technol Assess. 3: iii– 92.Google Scholar
Wakker P, Klaasen MP. 1995 Confidence intervals for cost-effectiveness ratios. Health Econ. 4: 373– 381.Google Scholar