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“Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting phase 2 and 3 trials. It is therefore crucial to ensure that these phases are conducted more efficiently, and that attrition rates are reduced. We argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of these tools — such as Bayesian methodologies — in early- and late-stage development are discussed, as well as the advantages and barriers to widespread implementation.“
Cyrus Mehta, Nitin Patel with fellow industry and academia co-authors, including PhRMA Adaptive Trial Working Group members
Nature Reviews Drug Discovery | AOP, published online 9, October 2009
“In recent years there has been increased industry interest and utilization of adaptive clinical trials. Although the term “adaptive” covers a large range of study features and designs, much of the current excitement is around designs that enable treatment groups to be dropped during the trial to enable more doses to be investigated and/or to reduce time between development phases using seamless designs.“
Nitin Patel, w/ Bill Byrom and Graham Nicholls of Perceptive Informatics
Applied Clinical Trials Magazine, an Advanstar publication, July, 2009
“We provide a method for obtaining confidence intervals, point estimates, and p-values for the primary effect size
parameter at the end of a two-arm group sequential clinical trial in which adaptive changes have been implemented along the
way. The method is based on applying the adaptive hypothesis testing procedure of Müller and Schåfer (2001, Biometrics 57,
886–891) to a sequence of dual tests derived from the stage-wise adjusted confidence interval of Tsiatis, Rosner, and Mehta
(1984, Biometrics 40, 797–803). In the nonadaptive setting this confidence interval is known to provide exact coverage. In
the adaptive setting exact coverage is guaranteed provided the adaptation takes place at the penultimate stage. In general,
however, all that can be claimed theoretically is that the coverage is guaranteed to be conservative. Nevertheless, extensive
simulation experiments, supported by an empirical characterization of the conditional error function, demonstrate convincingly
that for all practical purposes the coverage is exact and the point estimate is median unbiased. No procedure has previously
been available for producing confidence intervals and point estimates with these desirable properties in an adaptive group
sequential setting.“
Cyrus Mehta, w/ Werner Brannath and Martin Posch
Biometrics, The International Biometric Society, June, 2009
“This paper discusses the benefits and limitations of adaptive sample size re-estimation for late
stage confirmatory clinical trials. Comparisons are made with more traditional fixed sample
and group sequential designs. It is seen that the real benefit of the adaptive approach arises
through the ability to invest sample size resources into the trial in stages. The trial starts with
a small up-front sample size commitment. Additional sample size resources are committed to
the trial only if promising results are obtained at an interim analysis. This strategy is more advantageous than the fixed sample or group sequential approaches in certain
settings.“
Cyrus R. Mehta
Good Clinical Practice, an Informa UK Ltd. publication, 2009
“Because the clinical development process is enormously expensive and time consuming, there is considerable interest in statistical methods that use accumulating data from a clinical trial to inform and modify its design. Such redesign might include changes in target sample size and even changes in the target population. This article discusses developments in adaptive design of interest to cardiovascular research.“
Ping Gao, Cyrus R. Mehta and James H. Ware
Circulation, a Taylor & Francis Group publication, 2008
“We describe a method for sample size re-estimation at the penultimate stage of a group sequential design that achieves specified power against an alternative hypothesis corresponding to the current point estimate of the treatment effect.“
Ping Gao, Cyrus R. Mehta and James H. Ware
Journal of Biopharmaceutical Statistics, a Taylor & Francis Group publication, 2008
“A method for obtaining confidence intervals, point estimates and p-values for the primary effect size parameter at the end of a two-arm group sequential clinical trial in which adaptive changes have been implemented along the way.“
Werner Brannath, Cyrus R. Mehta and Martin Posch
Biometrics, Blackwell Publishing, 2008
"Proposing a method for computing conservative confidence intervals for a group sequential test in which an adaptive design change is made one or more times over the course of the trial."
Cyrus R. Mehta, Peter Bauer, Martin Posch and Werner Brannath
Statistics in Medicine, a Wiley InterScience publication, 2007
"Three adaptive methods for sample size re-estimation within a group sequential framework."
Cyrus R. Mehta and Nitin R. Patel
Statistics in Medicine, a Wiley InterScience publication, 2006
"Use of East software to evaluate properties of study designs with one or more interim analyses for futility."
Byron Jones, Pfizer Global Development, with G. Atkinson, J. Ward, E. Tan and T. Kerbusch
Pharmaceutical Statistics, a Wiley InterScience publication, 2006
"Adaptive designs have been advocated recently for monitoring clinical trials. We show that that one can improve uniformly on such adaptive designs using standard group-sequential tests based on the sequentially computed likelihood ratio test statistic."
Anastasios Tsiatis, North Carolina State Univ. and Cyrus Mehta, Cytel Software Corp.
Biometrika, (2003), 90, 2, pp. 367–378
Cyrus R. Mehta, Harvard University & Cytel Software Corporation
Presentated at The ASA-NJ's Spring Symposium, June 2002
Cyrus R. Mehta, Harvard University & Cytel Software Corp., Anastasios A. Tsiatis, North Carolina State University
Drug Information Journal, December 2001
Sandro Pampallona, ForMed, Statistics for Medicine; Anastasios A. Tsiatis, North Carolina State University; and Kyungmann Kim, University of Wisconsin
Drug Information Journal, December 2001
“We propose a method based on profile likelihood, where the likelihood is weighted by noninformative Jeffrey' prior. By doing extensive simulations, we find that the proposed method performs well compared to Wilson's method.”
Vivek Pradhan and Tathagata Banerjee
Communications in Statistics - Simulation and Computation, a Taylor & Francis publication, 2008
Thomas J. Santner, Vivek Pradhan, Pralay Senchaudhuri, Cyrus R. Mehta, and Ajit Tamhane Computational Statistics & Data Analysis 51 (2007) 5791 – 5799, August, 2007
S. Lydersen, V. Pradhan, P. Senchaudhuri and P. Laake
Statistics in Medicine 2007; 26:4328–4343, February, 2007
(Original Research)
John M. Boltri, Mark R. Akerson, Robert L. Vogel; Journal of Family Practice, August, 2002
Christopher J. Groves, Steven Wiltshire, Damian Smedley, Katherine R. Owen, Timothy M. Frayling, Mark Walker, Graham A. Hitman, Jonathan C. Levy, Stephen O'Rahilly, Stephan Menzel, Andrew T. Hattersley, Mark I McCarthy; Diabetes, May, 2003
Anders I Selden, Ylva Floderus, Lennart S. Bodin, H Kan B. Westberg, Stig Thunell
Archives of Environmental Health, July, 1999
Cyrus R. Mehta and Nitin R. Patel
Harvard University and Cytel Software Corporation
January 1, 1997
Christopher D.Corcoran, Utah State University; and
Cyrus R.Mehta, Harvard University and Cytel Software Corporation
Journal of Modern Statistical Methods, 2001
John Ludbrook, Univ. of Melbourne, Parkville, Victoria, Australia.
Clinical and Experimental Pharmacology and Physiology (2002) 29, pp 527-536 + addendum.
Tapabrata Maiti and Vivek Pradhan
Biometrics (2008) December publication pending
Dr. R.A. Ammann
Bone Marrow Transplantation (2004) 34, 277-278
Elizabeth N. King and Thomas P. Ryan
The American Statistician, August 2002, Vol. 56, No. 3, pp 163-170
Excerpt: ". . . maximum likelihood can produce very poor, even nonsensical, results under certain conditions."
Shelley B. Bull, Carmen Mak, Celiea M.T. Greenwood
Computational Statistics and Data Analysis, 39 (2002) pp 57-74
by Cyrus R. Mehta, Nitin R. Patel and Pralay Senchaudhuri
Journal of the American Statistical Association, March 2000, Vol. 95, No. 449, Theory and Methods, pp 99-108