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A hybrid procedure for detecting global treatment effects in multivariate clinical trials : theory and applications to fMRI studies
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Minas, Giorgos, Rigat, Fabio, 1975-, Nichols, Thomas E., Aston, John A. D. and Stallard, Nigel. (2012) A hybrid procedure for detecting global treatment effects in multivariate clinical trials : theory and applications to fMRI studies. Statistics in Medicine, Vol.31 (No.3). pp. 253-268. ISSN 0277-6715
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Official URL: http://dx.doi.org/10.1002/sim.4395
Abstract
In multivariate clinical trials, a key research endpoint is ascertaining whether a candidate treatment is more efficacious than an established alternative. This global endpoint is clearly of high practical value for studies, such as those arising from neuroimaging, where the outcome dimensions are not only numerous but they are also highly correlated and the available sample sizes are typically small. In this paper, we develop a two-stage procedure testing the null hypothesis of global equivalence between treatments effects and demonstrate its application to analysing phase II neuroimaging trials. Prior information such as suitable statistics of historical data or suitably elicited expert clinical opinions are combined with data collected from the first stage of the trial to learn a set of optimal weights. We apply these weights to the outcome dimensions of the second-stage responses to form the linear combination z and t tests statistics while controlling the test's false positive rate. We show that the proposed tests hold desirable asymptotic properties and characterise their power functions under wide conditions. In particular, by comparing the power of the proposed tests with that of Hotelling's T2, we demonstrate their advantages when sample sizes are close to the dimension of the multivariate outcome. We apply our methods to fMRI studies, where we find that, for sufficiently precise first stage estimates of the treatment effect, standard single-stage testing procedures are outperformed. Copyright © 2011 John Wiley & Sons, Ltd.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine > Warwick Medical School > Health Sciences Faculty of Science > Statistics Faculty of Medicine > Warwick Medical School Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) |
| Library of Congress Subject Headings (LCSH): | Multivariate analysis, Statistical hypothesis testing, Magnetic resonance imaging, Clinical trials |
| Journal or Publication Title: | Statistics in Medicine |
| Publisher: | John Wiley & Sons Ltd. |
| ISSN: | 0277-6715 |
| Date: | 10 February 2012 |
| Volume: | Vol.31 |
| Number: | No.3 |
| Page Range: | pp. 253-268 |
| Identification Number: | 10.1002/sim.4395 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| Funder: | University of Warwick. Centre for Analytical Science |
| Grant number: | EP/F034210/1 (UoW) |
| References: | 1. Macarron R, Banks MN, Bojanic D, Burns DJ, Cirovic DA, Garyantes T, Green DVS, Hertzberg RP, Janzen WP, Paslay JW et al. Impact of high-throughput screening in biomedical research. Nature Reviews Drug Discovery Mar 2011; 10(3):188–195. http://dx.doi.org/10.1038/nrd3368. 2. EvansWE, RellingMV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science Oct 1999; 286(5439):487–491. http://www.sciencemag.org/content/286/5439/487. 3. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science Oct 1999; 286(5439):531–537. http://ukpmc.ac.uk/abstract/MED/10521349. 4. Shendure J, Ji H. Next-generation DNA sequencing. Nature Biotechnology Oct 2008; 26(10):1135–1145. http://dx.doi. org/10.1038/nbt1486. 5. Ambrose J. Computerized transverse axial scanning (tomography): part 2. Clinical application. British Journal of Radiology Dec 1973; 46(552):1023–1047. http://bjr.birjournals.org/cgi/content/abstract/46/552/1023. 6. Strauss LG, Conti PS. The applications of PET in clinical oncology. Journal of Nuclear Medicine Apr 1991; 32(4): 623–648. http://jnm.snmjournals.org/cgi/content/abstract/32/4/623. 7. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics Apr 1993; 65:413–497. DOI: 10.1103/RevModPhys.65.413. 8. Matthews PM, Honey GD, Bullmore ET. Applications of fMRI in translational medicine and clinical practice. Nature Reviews Neuroscience Sep 2006; 7(9):732–744. http://dx.doi.org/10.1038/nrn1929. 9. Brideau C, Gunter B, Pikounis B, Liaw A. Improved statistical methods for hit selection in high-throughput screening. Journal of Biomolecular Screening 2003; 8(6):634–647. http://jbx.sagepub.com/content/8/6/634.abstract. 10. Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R. Statistical practice in high-throughput screening data analysis. Nature Biotechnology Feb 2006; 24(2):167–175. http://dx.doi.org/10.1038/nbt1186. 11. Pien HH, Fischman AJ, Thrall JH, Sorensen GA. Using imaging biomarkers to accelerate drug development and clinical trials. Drug Discovery Today Feb 2005; 10(4):259–266. 12. Bookheimer SY, StrojwasMH, CohenMS, Saunders AM, Pericak-Vance MA, Mazziotta JC, Small GW. Patterns of brain activation in people at risk for Alzheimer’s disease. New England Journal of Medicine 2000; 343:450–456. 13. Honey GD, Bullmore ET, Soni W, Varatheesan M, Williams SCR, Sharma T. Differences in frontal cortical activation by a working memory task after substitution of risperidone for typical antipsychotic drugs in patients with schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 1999; 96(23):13 432–13 437. 14. Davidson RJ, Irwin W, Anderle MJ, Kalin NH. The neural substrates of affective processing in depressed patients treated with venlafaxine. American Journal of Psychiatry 2003; 160(1):64–75. 15. Breiter HC, Gollub RL, Weisskoff RM, Kennedy DN, Makris N, Berke JD, Goodman JM, Kantor HL, Gastfriend DR, Riorden JP et al. Acute effects of cocaine on human brain activity and emotion. Neuron 1997; 19:591–611. 16. Iannetti GD, Zambreanu L, Wise RG, Buchanan TJ, Huggins JP, Smart TS, Vennart W, Tracey I. Pharmacological modulation of pain-related brain activity during normal and central sensitization states in humans. Proceedings of the National Academy of Sciences of the United States of America 2005; 102(50):18 195–18 200. 17. Jezzard P, Matthews PM, Smith SM. Functional MRI: An Introduction to Methods. Oxford University Press: Oxford, New York, 2001. 18. Simon R, Thall PF. Phase II Trials. Encyclopedia of Biostatistics. John Wiley & Sons, Ltd: New York, 2005. http://dx.doi.org/10.1002/0470011815.b2a01044. 19. ICH. International Harmonised Tripartite Guideline: Statistical principles for clinical trials E9, February 5, 1998. http://www.ich.org/LOB/media/MEDIA485.pdf. 20. Wise RG, Tracey I. The role of fMRI in drug discovery. Journal Of Magnetic Resonance Imaging 2006; 23:862–876. 21. Garry H, Bullmore E. Human pharmacological MRI. Trends in Pharmacological Sciences 2004; 25(7):366–374. DOI: 10.1016/j.tips.2004.05.009. http://www.sciencedirect.com/science/article/B6T1K-4CJ473C-4/2/0cf797776e15cba ff6ea011c660b44f3. 22. Whitcher B, Matthews P. Noninvasive brain imaging for experimental medicine in drug discovery and development: promise and pitfalls. International Journal of Pharmaceutical Medicine 2006; 20:167–175(9). 23. Huettel SA, Song AW, McCarthy G. Functional Magnetic Resonance Imaging. Sinauer Associates: Sunderland, Mass, 2004. 24. Poldrack RA, Mumford JA, Nichols TE. Handbook of Functional MRI Data Analysis. Cambridge University Press: Cambridge, 2011. 25. Poldrack RA. Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience 2007; 2:67–70. 26. Mitsis GD, Iannetti GD, Smart TS, Tracey I, Wise RG. Regions of interest analysis in pharmacological fMRI: how do the definition criteria influence the inferred result? Neuroimage 2007; 40:121–132. 27. Dmitrienko A, Tamhane AC, Bretz F (eds). Multiple Testing Problems in Pharmaceutical Statistics (Chapman & Hall/Crc Biostatistics Series). Chapman & Hall/CRC: Boca Raton, FL, 2009. 28. Sankoh AJ, D’Agostino RB, Huque MF. Efficacy endpoint selection and multiplicity adjustment methods in clinical trials with inherent multiple endpoint issues. Statistics in Medicine 2003; 22:3133–3150. 29. Pocock SJ, Geller NL, Tsiatis AA. The analysis of multiple endpoints in clinical trials. Biometrics 1987; 43:487–498. 30. Westfall PH, Krishen A, Young SS. Using prior information to allocate significance levels for multiple endpoints. Statistics in Medicine 1998; 17(18):2107–2119. http://dx.doi.org/10.1002/(SICI)1097-0258(19980930)17:18<2107:: AID-SIM910>3.0.CO;2-W. 31. Hotelling H. The generalization of Student’s ratio. The Annals of Mathematical Statistics 1931; 2(3):360–378. 32. Anderson TW. An Introduction to Multivariate Statistical Analysis, 2nd ed. John Wiley and Sons: New York, 2003. 33. O’Brien PC. Procedures for comparing samples with multiple endpoints. Biometrics 1984; 40:1079–1087. 34. D’Agostino RB, Russell HK. Multiple Endpoints, Multivariate Global Tests. Encyclopedia of Biostatistics. John Wiley & Sons, Ltd: New York, 2005. http://dx.doi.org/10.1002/0470011815.b2a13042. 35. Wassmera G, Reitmeirb CP, Kieserc M, Lehmachera W. Procedures for testing multiple endpoints in clinical trials: an overview. Journal of Statistical Planning and Inference 1986; 82:69–81. 36. Logan BR, Tamhane AC. On O’Brien’s OLS and GLS tests for multiple endpoints. Lecture Notes-Monograph Series 2004; 47:76–88. 37. Tang D, Geller NL, Pocock SJ. On the design and analysis of randomized clinical trials with multiple endpoints. Journal of the American Statistical Association 1993; 84:776–779. 38. Läuter J, Glimm E, Kroph S. New multivariate tests for data with an inherent structure. Biometrical Journal 1996; 38:1–23. 39. Friston K, Ashburner J, Kiebel S, Nichols T, Penny W. Statistical Parametric Mapping: The Analysis of Funtional Brain Images. Elsevier/Academic Press: Amsterdam, Boston, 2007. 40. Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: recommendations for good practice. Journal of Evaluation in Clinical Practice 2004; 10(2):307–312. http://dx.doi.org/10.1111/j..2002.384.doc.x. 41. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. Chapman and Hall/CRC: Boca Raton, Fla., 2004. 42. Spiegelhalter DJ, Freedman LS, Blackburn P. Monitoring clinical trials: conditional or predictive power? Controlled Clinical Trials 1986; 7:8–17. 43. Lehmann EL, Romano JP. Testing Statistical Hypotheses. Springer: New York, 2005. 44. Spiegelhalter DJ, Freedman LS. A predictive approach to selecting the size of a clinical trial. Statistics in Medicine 1986; 5:1–13. 45. O’Hagan A, Stevens JW. Bayesian assessment of sample size for clinical trials of cost-effectiveness. Medical Decision Making 2001; 21:219–230. 46. Huson LW. The Bayesian bootstrap in a predictive power analysis. Case Studies in Business, Industry and Government Statistics 2009; 3:18–22. 47. Kimani PK., Stallard N, Hutton JL. Dose selection in seamless phase II/III clinical trials based on efficacy and safety. Statistics in Medicine 2009; 28(6):917–936. http://dx.doi.org/10.1002/sim.3522. 48. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. JohnWiley and Sons: Chichester, 2004. 49. Shih WJ, Ohman-Strickland PA, Lin Y. Analysis of pilot and early phase studies with small sample sizes. Statistics in Medicine 2004; 23(12):1827–1842. http://dx.doi.org/10.1002/sim.1807. 50. Mardia KV, Kent JT, Bibby JM. Multivariate Analysis. Academic Press Inc.: London, New York, 1979. 51. Neuhauser M. How to deal with multiple endpoints in clinical trials. Fundamental and Clinical Pharmacology 2006; 20:515–523. |
| URI: | http://wrap.warwick.ac.uk/id/eprint/41134 |
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