Hypothesis testing by simulation of a medical study model using the expected net benefits criteria

Download Download as PDF (Size:670KB)  HTML   XML  PP. 364-374  
DOI: 10.4236/health.2013.53049    2,872 Downloads   4,729 Views   Citations


Introduction: This work investigates whether to conduct a medical study from the point of view of the expected net benefit taking into account statistical power, time and cost. The hypothesis of this paper is that the expected net benefit is equal to zero. Methods: Information were obtained from a pilot medical study that investigates the effects of two diagnostic modalities, magnetic resonance imaging (MRI) and computerized axial tomography scanner (CT), on patients with acute stroke. Statistical procedure was applied for planning and contrasting equivalence, non-inferiority and inequality hypotheses of the study for the effectiveness, health benefits and costs. A statistical simulation model was applied to test the hypothesis that conducting the study would or not result in overall net benefits. If the null hypothesis not rejected, no benefits would occurred and therefore the two arms-patterns of diagnostic and treatment are of equal net benefits. If the null hypothesis is rejected, net benefits would occur if patients are diagnosed with the more favourable diagnostic modality. Results: For any hypothesis design, the expected net benefits are in the range of 366 to 1796 per patient at 80% of statistical power if conducting the study. The power depends on the monetary value available for a unit of health improvement. Conclusion: The statistical simulations suggest that diagnosing patients with CT will provide more favourable health outcomes showing statistically significant expected net benefits in comparison with MRI.

Cite this paper

Abbas, I. , Rovira, J. and Casanovas, J. (2013) Hypothesis testing by simulation of a medical study model using the expected net benefits criteria. Health, 5, 364-374. doi: 10.4236/health.2013.53049.


[1] Peck, C.C. (1997) Drug development: Improving the process. Food and Drug Law Journal, 52, 163-167.
[2] Waller, D., Peake, M.D. and Stephens, R.J. (2004) Chemotherapy for patients with non-small cell lung cancer: The surgical setting of the Big Lung Trial. European Journal of Cardio-Thoracic Surgery, 26, 173-182. doi:10.1016/j.ejcts.2004.03.041
[3] Scagliotti, G.V., Fossati, R., Torri, V., Crinò, L., Giaccone, G., Silvano, G., Martelli, M., Clerici, M., Cognetti, F. and Tonato, M. (2003) Randomized study of adjuvant chemotherapy for completely resected stage I, II, or III: A non-small-cell lung cancer. JNCI Journal of the National Cancer Institute, 95, 1453-1461. doi:10.1093/jnci/djg059
[4] Girling, A.J., Lilford, R.J., Braunholtz, D.A. and Gillett, W.R. (2007) Sample-size calculations for studies that in form individual treatment decisions: A “true-choice” approach. Clinical Trials, 4, 15-24. doi:10.1177/1740774506075872
[5] Yin, K., Choudhary, P.K., Varghese, D. and Goodman, S.R. (2007) A Bayesian approach for sample size determination in method comparison studies. Statistics in Medicine, 27, 2273-2289. doi:10.1002/sim.3124
[6] Howard, G. (2007) Nonconventional clinical studies de signs: Approaches to provide more precise estimates of treatment effects with a smaller sample size, but at a cost. Stroke, 38, 804-808. doi:10.1161/01.STR.0000252679.07927.e5
[7] Berry, D.A. (2005) Introduction to Bayesian methods III: Use and interpretation of Bayesian tools in design and analysis. Clinical Trials, 2, 295-300. doi:10.1191/1740774505cn100oa
[8] Tan, S.B. and Machin, D. (2002) Bayesian two-stage de signs for phase II clinical studies. Statistics in Medicine, 21, 1991-2012. doi:10.1002/sim.1176
[9] Patel, N.R. and Ankolekar, S. (2007) A Bayesian approach for incorporating economic factors in sample size de sign for clinical studies of individual drugs and portfolios of drugs. Statistics in Medicine, 26, 4976-4988. doi:10.1002/sim.2955
[10] Leung, D.H. and Wang, Y.G. (2001) A Bayesian decision approach for sample size determination in phase II studies. Biometrics, 57, 309-312. doi:10.1111/j.0006-341X.2001.00309.x
[11] Shao, Y., Mukhi, V. and Goldberg, J.D. (2008) A hybrid Bayesian-frequentist approach to evaluate clinical studies designs for tests of superiority and non-inferiority. Statis tics in Medicine, 27, 504-519. doi:10.1002/sim.3028
[12] Kikuchi, T., Pezeshk, H. and Gittins, J. (2008) A Bayesian cost-benefit approach to the determination of sample size in clinical studies. Statistics in Medicine, 27, 68-82. doi:10.1002/sim.2965
[13] Jiang, H., Liu, Y. and Su, Z. (2009) An optimization algorithm for designing phase I cancer clinical studies. Con temporary Clinical Trials, 29, 102-108. doi:10.1016/j.cct.2007.06.003
[14] Huang, X., Biswas, S., Oki, Y., Issa, J.P. and Berry, D.A. (2007) A parallel phase I/II clinical studies design for combination therapies. Biometrics, 63, 429-436. doi:10.1111/j.1541-0420.2006.00685.x
[15] Baker, S.G. and Heidenberger, K. (1989) Choosing sample sizes to maximize expected health benefits subject to a constraint on total studies costs. Medical Decision Making, 9, 14-25. doi:10.1177/0272989X8900900104
[16] Spiegelhalter, D.J. and Best, N.G. (2003) Bayesian approaches to multiple sources of evidence and uncertainty in complex cost-effectiveness modelling. Statistics in Medicine, 22, 3687-3709. doi:10.1002/sim.1586
[17] Briggs, A. and Sculpher, M. (1997) Markov models of medical prognosis—Commentary. British Medical Journal, 314, 354-355. doi:10.1136/bmj.314.7077.354a
[18] Willan, A.R. and Pinto, E.M. (2005) The value of information and optimal clinical trial design. Statistics in Medicine, 24, 1791-1806. doi:10.1002/sim.2069
[19] Parody, E.R. (2007) Análisis del coste-utilidad de la re sonancia magnética en el manejo del paciente con isque mia cerebral aguda. Universitat Autónoma de Barcelona.
[20] Fagan, S.C., Morgenstern, L.B., Petita, A., Ward, R.E., Tilley, B.C., Marler, J.R., et al. (1998) Cost-effectiveness of tissue plasminogen activator for acute ischemic stroke. Neurology, 50, 883-890. doi:10.1212/WNL.50.4.883
[21] Pinto-Prades, J. and Abellán-Perpi?án, J. (2005) Measuring the health of pop-ulations: The veil of ignorance approach. Health Eco-nomics, 14, 69-82. doi:10.1002/hec.887

comments powered by Disqus

Copyright © 2017 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.