Advanced Statistical Methods Publications from 2008 forward
Visit our Publications Archive for peer-reviewed scientific articles prior to 2008
Adaptive Clinical Trial Methods
Sponsored by Sunesis Pharmaceuticals, the VALOR pivotal phase 3 study employs an innovative "Promising Zone" adaptive strategy and is a directly attributable result of Cytel's pioneering research in sample size re-estimation designs. The adaptive approach benefits study sponsors by enabling conditional investment of sample size resources (costs associated with the number of patients enrolled) in pre-determined stages. Starting with a comparatively conservative patient commitment, additional subjects are added to the trial only if justified by the interim analysis data results.
Cyrus R. Mehta and co-authors at Cytel and Sunesis Pharmaceuticals
Trials in Progress Poster Session | American Society of Clinical Oncology Annual Convention| June 6, 2011
“We consider a clinical trial with a primary and a secondary endpoint where the secondary endpoint is tested only if the primary endpoint is significant. The trial uses a group sequential procedure with two stages. The familywise error rate (FWER) of falsely concluding significance on either endpoint is to be controlled at a nominal level ?. The type I error rate for the primary endpoint is controlled by choosing any ?-level stopping boundary, e.g., the standard O'Brien-Fleming or the Pocock boundary. Given any particular ?-level boundary for the primary endpoint, we study the problem of determining the boundary for the secondary endpoint to control the FWER. Numerical studies indicate that the O'Brien-Fleming
boundary for the primary endpoint and the Pocock boundary for the secondary endpoint generally gives the best primary as well as secondary power performance. A clinical trial example is given to illustrate the methods.“
Ajit Tamhane, Northwestern Univ., Cyrus R. Mehta and Lingyun Liu, Cytel Inc.
Biometrics | The Journal of the ISBS | December, 2010
“This paper discusses the benefits and limitations of adaptive sample size re-estimation for phase 3 confirmatory clinical trials. 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.
Starting 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 shown through examples of an actual neurology and cardiology trial to be more advantageous than the fixed sample or group sequential approaches in certain settings. Therefore, we define those promising circumstances in which a conventional final inference can be performed while preserving the overall type-1.“
Cyrus R. Mehta and Stuart J. Pocock, London School of Hygiene and Tropical Medicine
Statistics in Medicine | a John Wiley publication | In Press as of September, 2010
“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 industry and academia co-authors, including PhRMA Adaptive Trial Working Group members
Nature Reviews Drug Discovery | AOP | published online 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, with 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
Group Sequential Trial Methods
“We consider a clinical trial with a primary and a secondary endpoint where the secondary endpoint is tested only if the primary endpoint is significant. The trial uses a group sequential procedure with two stages. The familywise error rate (FWER) of falsely concluding significance on either endpoint is to be controlled at a nominal level ?. The type I error rate for the primary endpoint is controlled by choosing any ?-level stopping boundary, e.g., the standard O'Brien-Fleming or the Pocock boundary. Given any particular ?-level boundary for the primary endpoint, we study the problem of determining the boundary for the secondary endpoint to control the FWER. Numerical studies indicate that the O'Brien-Fleming
boundary for the primary endpoint and the Pocock boundary for the secondary endpoint generally gives the best primary as well as secondary power performance. A clinical trial example is given to illustrate the methods.“
Ajit Tamhane, Northwestern Univ., Cyrus R. Mehta and Lingyun Liu, Cytel Inc.
Biometrics | The Journal of the ISBS | December, 2010
Exact Inference Methods
“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
Logistic Regression
Tapabrata Maiti and Vivek Pradhan
Biometrics (2008) December