期刊论文详细信息
BMC Medical Research Methodology
Comparing multiple competing interventions in the absence of randomized trials using clinical risk-benefit analysis
Douglas A Coyle3  Philip S Wells3  Tim Ramsay3  Nicholas J Barrowman1  Marc A Rodger3  Alejandro Lazo-Langner2 
[1]Chalmers' Research Group, Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Rd, Ottawa ON, K1H 8L1, Canada
[2]Department of Epidemiology and Biostatistics, University of Western Ontario, 800 Commissioners Rd E, London ON, N6A 5W9, Canada
[3]Clinical Epidemiology Program, Ottawa Health Research Institute, 725 Parkdale Ave, Ottawa ON, K1Y 4E9, Canada
关键词: indirect comparison;    Risk;    Monte Carlo Method;    Methods;    Meta-Analysis;    Decision Making;    Risk-Benefit Analysis;   
Others  :  1136852
DOI  :  10.1186/1471-2288-12-3
 received in 2010-10-22, accepted in 2012-01-10,  发布年份 2012
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【 摘 要 】

Background

To demonstrate the use of risk-benefit analysis for comparing multiple competing interventions in the absence of randomized trials, we applied this approach to the evaluation of five anticoagulants to prevent thrombosis in patients undergoing orthopedic surgery.

Methods

Using a cost-effectiveness approach from a clinical perspective (i.e. risk benefit analysis) we compared thromboprophylaxis with warfarin, low molecular weight heparin, unfractionated heparin, fondaparinux or ximelagatran in patients undergoing major orthopedic surgery, with sub-analyses according to surgery type. Proportions and variances of events defining risk (major bleeding) and benefit (thrombosis averted) were obtained through a meta-analysis and used to define beta distributions. Monte Carlo simulations were conducted and used to calculate incremental risks, benefits, and risk-benefit ratios. Finally, net clinical benefit was calculated for all replications across a range of risk-benefit acceptability thresholds, with a reference range obtained by estimating the case fatality rate - ratio of thrombosis to bleeding.

Results

The analysis showed that compared to placebo ximelagatran was superior to other options but final results were influenced by type of surgery, since ximelagatran was superior in total knee replacement but not in total hip replacement.

Conclusions

Using simulation and economic techniques we demonstrate a method that allows comparing multiple competing interventions in the absence of randomized trials with multiple arms by determining the option with the best risk-benefit profile. It can be helpful in clinical decision making since it incorporates risk, benefit, and personal risk acceptance.

【 授权许可】

   
2012 Lazo-Langner et al; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Eddy DM: Clinical decision making: from theory to practice. Anatomy of a decision. JAMA 1990, 263:441-443.
  • [2]Lynd LD, O'Brien BJ: Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis. J Clin Epidemiol 2004, 57:795-803.
  • [3]Holden WL, Juhaeri J, Dai W: Benefit-risk analysis: a proposal using quantitative methods. Pharmacoepidemiol Drug Saf 2003, 12:611-616.
  • [4]Bender R: Calculating confidence intervals for the number needed to treat. Control Clin Trials 2001, 22:102-110.
  • [5]Lesaffre E, Pledger G: A note on the number needed to treat. Control Clin Trials 1999, 20:439-447.
  • [6]Holden WL, Juhaeri J, Dai W: Benefit-risk analysis: examples using quantitative methods. Pharmacoepidemiol Drug Saf 2003, 12:693-697.
  • [7]Shakespeare TP, Gebski VJ, Veness MJ, Simes J: Improving interpretation of clinical studies by use of confidence levels, clinical significance curves, and risk-benefit contours. Lancet 2001, 357:1349-1353.
  • [8]Willan AR, O'Brien BJ, Cook DJ: Benefit-risk ratios in the assessment of the clinical evidence of a new therapy. Control Clin Trials 1997, 18:121-130.
  • [9]Stinnett AA, Mullahy J: Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 1998, 18:S68-S80.
  • [10]Briggs AH, Mooney CZ, Wonderling DE: Constructing confidence intervals for cost-effectiveness ratios: an evaluation of parametric and non-parametric techniques using Monte Carlo simulation. Stat Med 1999, 18:3245-3262.
  • [11]Shaffer M, Watterberg K: Joint distribution approaches to simultaneously quantifying benefit and risk. BMC Med Res Methodol 2006, 6:48. BioMed Central Full Text
  • [12]Bucher HC, Guyatt GH, Griffith LE, Walter SD: The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol 1997, 50:683-691.
  • [13]Song F, Altman DG, Glenny AM, Deeks JJ: Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. Br Med J 2003, 326:472.
  • [14]Brennan A, Akehurst R: Modelling in health economic evaluation. What is its place? What is its value? Pharmacoeconomics 2000, 17:445-459.
  • [15]Doubilet P, Begg CB, Weinstein MC, Braun P, McNeil BJ: Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach. Med Decis Making 1985, 5:157-177.
  • [16]Drummond MF, O'Brien B, Stoddart GL, Torrance GW: Collection and analysis of data. In Methods for the economic evaluation of health programmes. Edited by Drummond MF, O'Brien B, Stoddart GL, Torrance GW. Oxford UK: Oxford University Press; 1997:232-264.
  • [17]Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ, Stoddart GL: Methods for the economic evaluation of health care programmes. 3rd edition. Oxford, UK: Oxford University Press; 2005.
  • [18]Black WC: The CE plane: a graphic representation of cost-effectiveness. Med Decis Making 1990, 10:212-214.
  • [19]Briggs AH: Handling uncertainty in cost-effectiveness models. Pharmacoeconomics 2000, 17:479-500.
  • [20]Geerts WH, Bergqvist D, Pineo GF, Heit JA, Samama CM, Lassen MR, et al.: Prevention of Venous Thromboembolism: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest 2008, 133:381S-453.
  • [21]Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF: Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. Quality of Reporting of Meta-analyses. Lancet 1999, 354:1896-1900.
  • [22]Lazo-Langner A, Coyle D, Barrowman NJ, Ramsay T, Wells PS, Scarvelis D, et al.: Clinical outcomes in patients receiving venous thromboembolism (VTE) prophylaxis after orthopedic surgery (OS). A systematic review and meta analysis of proportions. Blood 2006, 108:Abstract 626.
  • [23]The Columbus Investigators: Low-molecular-weight heparin in the treatment of patients with venous thromboembolism. N Engl J Med 1997, 337:657-662.
  • [24]Schulman S, Kearon C: Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non-surgical patients. J Thromb Haemost 2005, 3:692-694.
  • [25]Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al.: Assessing the quality of reports of randomized clinical trials: Is blinding necessary? Control Clin Trials 1996, 17:1-12.
  • [26]Schulz KF, Grimes DA: Allocation concealment in randomised trials: defending against deciphering. Lancet 2002, 359:614-618.
  • [27]Farnum NR, Stanton LW: Some Results Concerning the Estimation of Beta Distribution Parameters in PERT. J Oper Res Soc 1987, 38:287-290.
  • [28]Indurkhya A, Mitra N, Schrag D: Using propensity scores to estimate the cost-effectiveness of medical therapies. Stat Med 2006, 25:1561-1576.
  • [29]Macario A, Chow JL, Dexter F: A Markov computer simulation model of the economics of neuromuscular blockade in patients with acute respiratory distress syndrome. BMC Med Inform Decis Mak 2006, 6:15. BioMed Central Full Text
  • [30]Moher D, Schultz KF, Altman DG: The CONSORT statement: revised recommendation for improving the quality of reports of parallel-group randomised trials. Lancet 2001, 357:1191-1194.
  • [31]Moher D, Jadad AR, Nichol G, Penman M, Tugwell P, Walsh S: Assessing the quality of randomized controlled trials: an annotated bibliography of scales and checklists. Control Clin Trials 1995, 16:62-73.
  • [32]Garg AX, Suri RS, Barrowman N, Rehman F, Matsell D, Rosas-Arellano MP, et al.: Long-term renal prognosis of diarrhea-associated hemolytic uremic syndrome: a systematic review, meta-analysis, and meta-regression. JAMA 2003, 290:1360-1370.
  • [33]Laird NM, Mosteller F: Some statistical methods for combining experimental results. Int J Technol Assess Health Care 1990, 6:5-30.
  • [34]Jakubczyk M, Kaminski B: Cost-effectiveness acceptability curves - caveats quantified. Health Econ 2010, 19:955-963.
  • [35]Moreno E, Giron FJ, Vazquez-Polo FJ, Negrin MA: Optimal healthcare decisions: Comparing medical treatments on a cost-effectiveness basis. European Journal of Operational Research 2010, 204:180-187.
  • [36]Newcombe RG: Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 1998, 17:857-872.
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