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Cyrus Mehta on the Founding of Cytel

Cyrus Mehta on the Founding of Cytel


October 24, 2022
Cyrus Mehta, co-founder of Cytel
Cytel
 ,
Cyrus Mehta

 

On the occasion of Cytel’s 35th anniversary, co-founder Professor Cyrus Mehta discusses the founding of Cytel, the evolution of the industry over the last 35 years, and the ongoing innovations in software and statistical strategy.

Cytel was founded after an extensive collaboration between you and Nitin Patel in places like the Dana Farber Cancer Research Institute. What were the problems you were collaborating on that led to the founding of Cytel?

We were primarily collaborating on developing computational methods to perform inferences on small categorical data sets, i.e., exact tests for categorical data. These problems arose from my work at the Dana Farber Cancer Institute, where we had to make decisions on whether a new drug was safe relative to the controlled drug. The data was classified in terms of the levels of toxicity that were obtained from these clinical trials, and the categories were no toxicity, mild toxicity, moderate toxicity, severe toxicity, and drug death, in that order. There were very few drugs in the high categories, and more in the lower categories. Analyzing that data where you had zeroes and small numbers in higher categories could not be done using the traditional large sample methods. These problems led us to tackle categorical data differently, primarily by using permutation tests.

What was the statistical landscape like in the pharmaceutical industry at Cytel’s founding? How does it compare to the landscape now?

It is completely different now. In the 1980s, and part of the 1990s, too, clinical trials were outsourced by pharmaceutical companies to the National Institutes of Health. The National Institutes of Health funded cooperative groups that were academic centers, especially in oncology, and they grouped together and pulled their patients into these clinical trials. Multiple institutions formed one cooperative group, such as the Eastern Cooperative Oncology Group, the Radiotherapy Oncology Group, or the Children’s Leukemia Group A. These groups had a coordination center, an operations office, and a statistical center, and these different institutions ran the trials, funneled all the data through them, and developed reports.

This model gradually changed over time as the pharmaceutical companies began to conduct clinical trials themselves, with their own data. Today, I believe, there is very little work done by the cooperative groups, and almost all the trials are sponsored by pharmaceutical companies.

How did Cytel move from small sample to group sequential to adaptive design expertise?

It started as a business opportunity where we had to expand our offerings as it was not sufficient to just provide statistical software for small sample problems. There was not enough scope for consulting and no design component to it. We were providing tools for analyzing small, sparse, and incomplete data. Hence, pharmaceutical companies could use our software StatXact and LogXact to analyze the data on their own. The only consulting opportunity we had was in training them. On the other hand, group sequential problems had a larger impact on the business of the sponsoring organization, because with group sequential design you could get your products to the market faster with fewer patients. So, it was a bigger opportunity for us.

The methodology for these group sequential methods was developed by some of the statisticians at Harvard, where I am a faculty member. That allowed me to collaborate with some of those statisticians and persuade them to act as consultants to Cytel in the early days when we were developing the East software. Later, these group sequential methods evolved further into adaptive methods. Again, we sensed this as an opportunity for Cytel, and our East software evolved further into providing solutions not just for group sequential designs, but for adaptive group sequential designs.

 

How was East created and how did it become the flagship product that it is today?

In those days, we used to conduct an annual seminar with the pharmaceutical industry. There was a pharmaceutical company called Schering-Plough Corporation, which is now a part of Merck. The biostatistics departments at Harvard and Schering-Plough used to have an annual, two-day symposium at Harvard on different important topics. One of the early ones was on group sequential designs and, subsequently, there were other such workshops on missing data, multiple comparisons problems, and numerous other important statistical issues that needed solutions. The workshop on designing group sequential clinical trials inspired me to develop a software based on the methods that were being presented at that symposium, which was perhaps in the early or mid-1990s. I was able to gain support from the National Cancer Institute through their program called the Small Business Innovation Research Program. We were able to apply for a grant to develop the East software through this program.

During this process, we collaborated with Anastasios (Butch) Tsiatis, Kyungmann Kim, and Sandro Pampallona, outstanding experts on group sequential methods, who were at Harvard at that time. We also received a lot of advice from David DeMets, who developed the famous Lan and DeMets error spending function. Chris Jennison and Bruce Turnbull, who have written the standard textbook on this topic, also extended their support, engaged in technical discussions with us, and offered joint workshops with us to industry statisticians.

 

One of Cytel’s greatest innovations is undoubtedly within the realm of sample size re-estimation. What was the innovation of the promising zone?

Around the turn of the century, some papers were being published on adaptive methods. Adaptive methods were the next evolution of group sequential methods, in the sense that one was now able to look at the interim data and adapt the sample size of the study, the number of looks that were needed or the spacing of the looks. All of this could be done unblinded because there was a model already set up for doing unblinded interim analysis in group sequential designs. An independent data monitoring committee could look at the data, without revealing anything to the pharmaceutical sponsor. That made it possible to develop more innovative designs than simply stopping a trial early for overwhelming efficacy or futility.

There was now an operational process for looking at interim data, while at the same time many papers were published on statistical methodology for making adaptive changes based on the interim looks, without inflating the type one error. These papers made it clear that this was a good opportunity for East. Hence, we started putting these methods into East, and the word Promising Zone was coined by my colleague, Stuart Pocock. We wrote a paper on sample size re-estimation, and we called it the Promising Zone Method. We proposed that the best way to implement sample size re-estimation was to partition the interim data into zones. So, if the interim data were excellent, you need not make any change in the sample size. Also, if the interim data were quite bad, you need not make any change in the sample size. In the first case, you could stop early for efficacy or just continue unchanged. In the second case, you might stop early for futility or continue unchanged.

There would be a sweet spot in the middle where it might be advantageous to increase the sample size, because having seen the interim data, it may appear that you could increase power. In this case, it would be called conditional power, that is, conditional on what you would have already seen. So, there would be a good opportunity to increase the sample size and boost the conditional power back to the desired 90% that the sponsor would be interested in. The Promising Zone was defined as the zone in which we could boost the conditional power. Outside of this zone, it would not boost the power high enough, be too expensive, or the conditional power might already be super high, and it would not be necessary to increase the sample size.

 

Some people don’t realize that there are still innovations being added to the promising zone and to sample size re-estimation methods in general, and that these will create ripples across the industry. Would you be able to highlight some of what we will see in the next few years?

There has been a lot of research parallel to Adaptive Designs, on testing of multiple end points, or multiple treatment arms. It has been observed that in a clinical trial, you will gain a lot of efficiency if you ask more than one question about what is it that you want to discover about the new treatment, relative to the standard of care. At the same time, preserving the integrity of the family-wise error rate, which is the equivalent in multiple testing of the type one error in a two-arm simple trial. This methodology has been developed, but what is needed now is to combine this methodology with the interim analysis.

Group sequential and adaptive methods focus on taking interim looks at the data and using them to make modifications. Multiple testing has been developed for no interim analysis, it allows single look at the data and asking multiple questions at the end of the study, such as, which of many sub-groups is doing better, or if you know that the treatment is already good for overall survival, is it also good for progression free survival? Or the other way around. Does the presence or absence of a genetic mutation make a difference to the patient’s response compared to the population at large? A number of such multiple questions can be asked. If you combine this opportunity with the interim analysis, you can refine the questions and focus the sample size only on those questions that look interesting from the interim analysis point of view. Hence, the promise is that you combine the methods of adaptive design with the available statistical research that has already emerged on multiple testing, but for which there has been no interim analysis.

 

How do you think software and statistical strategy will shape clinical trials in the next few years?

There is already a huge community building open-source software, but that software, which is useful for research and to explore new ideas, is probably not ready for regulatory submissions. For regulatory submissions, one needs software that has been vetted and seen to be valid from a regulatory perspective. That is where we have been very successful with East. We are moving forward with our latest software at Cytel called Solara, which makes it possible to efficiently, intuitively, and quickly explore all the different multiple testing and group sequential options that are available under different scenarios of treatment effects, enrollment rates, and other unknown co-variants.

With Solara, you can have a possible design space of thousands of trials and use technology to explore all of them very quickly and find the ones that are worth pursuing further. This is an example of how software can combine with statistical methodology. We are developing many different statistical methods that give us lots of options for design parameters, and we are developing software that allows you to quickly generate all these different statistical models, and then explore them quickly with a nice visualization. In the end, you can have the best possible design for any given situation.

 

 

In the past few years, you have occasionally argued that private sector companies, even smaller biotechs, could evolve to make use of the advantages of multi-arm and platform trials. How would this work and how would Cytel help?

At present, I think they need a neutral party to set up the infrastructure for a platform trial. If it is a pharmaceutical company that sets up the platform, then they will only be testing their own drugs on it, and it will not be a collaboration with different industry partners. An academic center can probably be the neutral partner. A good model is the STAMPEDE Trial where the Medical Research Council in England had set up the infrastructure. In this trial different molecules from different companies were tested over a twenty-year period for prostate cancer.

Many successful drugs were discovered in this manner as it did not require going through the Phase II and Phase III settings. You can have many patients right up front, and you can test the new molecules with large sample sizes against the standard control arm. These molecules did not have to be tested in sequence. Instead, you could test several at a time against a common control arm. The winners would be taken off the trial, and then be available to the patients, and new molecules could take their place.

When we attended the ASCO meetings every year, we would find that very often there would be a new discovery from the STAMPEDE Trial, and it would be announced at ASCO. The reason this works is because there are plenty of new molecules coming out from small biotech companies and large pharmaceutical companies. Hence, when you have a plethora of molecules, you can test them simultaneously against standard of care, rather than testing sequentially. These are exciting new molecules and there is no shortage of physicians who want to put their patients on these trials. Consequently, there is no difficulty in recruiting patients and finding new molecules. In fact, in the Medical Research Council, they have very strict criteria for which new molecules they will accept for testing because they get requests from several companies.

 

Within therapeutic areas, Cytel’s greatest contributions have arguably been in oncology and cardiovascular. How have these areas developed in the past 35 years and how has Cytel contributed?

Cardiovascular trials have advanced in a very specific way. The clinicians who work in the cardiovascular area have become more sophisticated about statistical methods. They love partnering and working with statisticians. They also understand the data really well, and I would say, much more than the oncologists. They have used group sequential methods a lot and have no difficulties with it. But they have not looked at biomarkers yet and are now looking to start designing trials where they can look at biomarkers and reduce the size of their trials. Typically, their trials have been quite large because they have been so successful with their treatments.

There is a group called the Heart Failure Collaboratory, which is a public-private partnership between statisticians and cardiologists. They meet once a year, and they are now starting to look at more sophisticated problems. Typically, they have been looking at time-to-event trials and the standard has always been to make the assumption that the hazard ratio of the treatment to the control arm is constant. They have discovered in recent trials that this assumption does not always hold, and are starting to look at new methods, in partnership with experts on these questions. How will you analyze these data if the proportional hazard assumption is not valid? I think there is a good opportunity here for Cytel to work one step earlier. How will you design trials where you do not know whether the proportional hazards assumption will hold or not? If you can design such trials, where the design is so good that if the proportional hazard assumption holds, you have the right sample size, and if it does not hold, you still have the right sample size, that will be a holy grail. Some of us have been working on that question.

In Oncology, they have moved further ahead than cardiology, in terms of looking at biomarkers. They recognized long ago that large oncology trials are not successful. The population is too heterogeneous, so they are looking at starting with smaller subgroups and trying to develop methods where the molecule is targeted at specific biomarkers only. Here, there are opportunities for adaptive designs, which we have been involved with, but only with the smaller biotech companies. Large pharmaceutical companies are generally more cautious about using new methods.

 

As it is a 35-year anniversary celebration, where do you think the industry will be in 2050 (about 35 years from now)?

That is a very difficult question. Technology advances at an exponential pace. This acceleration is something that my friend, Ray Kurzweil, has been demonstrating over the years with specific examples from different fields. If you look at how we have moved, for instance, from doing computations on a slide rule to 50 years later doing computations in the Cloud, then the kind of computing power that will be available in 30 or 50 years from now is unimaginable. How the biopharmaceutical industry will exploit this power is another question. My guess is that they will be able to use predictive models very successfully. But again, this will be combined with medical advances, which are also accelerating. The new therapies are developed much more rapidly now. Hence, it is very hard to predict what will happen 50 years from now.

 

Are there any words of wisdom you would like to share with young people in the field now?

I think young people should follow their passion and pursue their interests in research, and not think of it as just a job. If they’re interested in something and find it promising, even if it is not fashionable, then they should pursue it and not worry about whether it is going to be popular. They should believe in it themselves and follow their instinct.

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Cyrus Mehta, co-founder of Cytel

Cyrus Mehta

Co-Founder and President

“We started the company with certain values that are important to us: quality, integrity and empathy with the customer. We still want to do the best possible job that we can – there is a tremendous amount of energy generated by the interaction between us as a team.”

Cyrus Mehta is a prominent biostatistician, and Fellow of the ​American Statistical Association. He co-founded Cytel Inc. with ​Nitin Patel in 1987, with a shared vision to make modern methods in statistics and operations research accessible to clinical researchers, by creating quality software for statistical analyses.

Cyrus’s efforts helped establish Cytel as an industry leader in exact statistics, adaptive and group sequential methods. He remains a driving force behind ​Cytel’s East®, the industry-standard software for trial design, simulation and monitoring.

Experience

As one of the world’s leading experts on ​​adaptive clinical trials, Cyrus regularly provides guidance and training to leading pharmaceutical companies, academic collaborators and FDA personnel. He has published 100+ research articles in scientific journals including JASA, Biometrics, Biometrika, Circulation, The Lancet, The New England Journal of Medicine and Statistics in Medicine.

Cyrus is an adjunct professor of biostatistics at the Harvard T.H. Chan School of Public Health and holds degrees from the Massachusetts Institute of Technology and the Indian Institute of Technology at Bombay. His work has resulted in groundbreaking innovations in computational statistics for rare events and statistical design of adaptive trials. He and his co-authors received the ASA’s 1987 George W. Snedecor Award for the best paper in biometry.

Research

Cyrus was a chief contributor to Cytel’s development of permutational algorithms and their applications to categorical data analysis, nonparametric tests, power and sample size calculations, contingency tables analysis and, more generally, to inference on the parameters in regression models for categorical data. The same algorithms make it computationally feasible to obtain accurate p-values, confidence intervals and sample-size designs for small or unbalanced data sets and sparse contingency tables. These advances have revolutionized general statistical practices.

His recent research focuses on developing group-sequential and adaptive trial methods and supporting software, including adaptive sample size re-estimation or “Promising Zone” designs.

Awards & Recognitions

  • Distinguished Alumni Award from Indian Institute of Technology, Bombay (2016)
  • Lifetime Achievement Award from the International Indian Statistics Association (2015)
  • Zoroastrian Entrepreneur of the Year (2002)
  • Received the George W. Snedecor Award from American Statistical Assoc. (1987)
  • Fellow of the American Statistical Association (Elected)
  • Member of the International Statistical Institute (Elected)
  • Mostellar Statistician of the Year (Massachusetts Chapter of the American Statistical Association)

Publications

  1. Mehta CR, Mukhopadhyay A, Posch M. Graph based multi arm, multiple endpoint, two stage design. Statistics in Medicine. 2025. DOI: https://doi.org/10.1002/sim.70237
  2. Mukherjee R, Muehlemann N, Gao Y, Stone GW, Mehta CR. Adaptive Design with Bayesian Informed Interim Decisions: Application to a Randomized Trial of Mechanical Circulatory Support. Therapeutic Innovation and Regulatory Science. 59.6 (2025): 1516-1525. 2025.
  3. Mehta CR, Kappler M. A comparison of two methods for adaptive multi-arm two-stage designs. Statistics in Medicine, http://dx.doi.org/10.1002/sim.70162 2025.
  4. Tamhane AC, Dong Xi, Mehta CR, Romanenko A. Testing one primary and two secondary endpoints in a two-stage group sequential trial with extensions. Statistics in Medicine. https://doi.org/10.1002/sim.10346 2025.
  5. Razaghizad A, Haya A, Guang KZ, Ferreira JP, White WB, Mehta CR, Bakris G, Zannad F, Sharma A. Pathophysiological sex-differences in heart failure progression after acute coronary syndrome: insights from the EXAMINE trial. Journal of Cardiac Failure. Available online 7 Nov 2023. https://doi.org/10.1016/j.cardfail.2023.10.474.
  6. Park JH, Sharif B, Harari O, … Mehta CR, Wathen Kyle. Economic evaluation of cost and time required for a platform trial vs conventional trials. JAMA Network Open; July 2022, Vol 5, No 7.
  7. Nelson BS, Liu L, Mehta CR. A simulation-based comparison of estimation methods for adaptive and classical group sequential clinical trials. Pharmaceutical Statistics, 2022 May; 21(3):599-611.
  8. Razaghizad A, Sharma A, Ferreira JP, White WB, Mehta CR, Bakiris GL, Zannad F. External validation and extension of the TIMI risk score for heart failure in diabetes for patients with recent acute coronary syndrome: an analysis of the EXAMINE trial. Diabetes, Obesity and Metabolism, 2023; 25:229-237
  9. Mehta CR, Bhingare A, Liu L, Senchaudhuri P. Optimal Adaptive Promising Zone Designs. Statistics in Medicine, 2022; vol 41, 1950-1970.
  10. Ghosh P. Ristl R, König F, Posch M, Jennison C, Götte H, Schüler, Mehta CR. Robust group sequential designs for trials with survival endpoints and delayed response. Biometrical Journal, 2022; vol 64, 343-360.
  11. Ferreira JP, Rossignol P, Bakris G, Mehta CR, White WB, Zannad F. Body weight changes in patients with type 2 diabetes and a recent acute coronary syndrome: an analysis from the EXAMINE trial. Cardiovascular Diabetology (2021). In Press.
  12. Ferreira JP, Rossignol P, Bakris G, Mehta CR, White WB, Zannad F. Blood and urine biomarkers predicting worsening kidney function in patients with type 2 diabetes post-acute coronary syndrome: an analysis from the EXAMINE trial. American Journal of Nephrology (2021). In Press.
  13. Olshansky B, Bhatt DL, …, Mehta CR, …, Chung MK. REDUCE-IT INTERIM: accumulation of data across prespecified interim analyses to final results. European Heart Journal – Cardiovascular Pharmacotherapy (2021) 7, e61-e63. doi:10.1093/ehjcvp/pvaa118
  14. Ferreira JP, Rossignol P, Sharma, A, White WB, Mehta CR, Bakris G, Zannad, F. Red Cell Distribution Width in Patients with Diabetes and Myocardial Infarction: an analysis from the EXAMINE trial. Diabetes, Obesity and Metabolism, 2021. https://doi.org/10.1111/dom.14371
  15. Januzzi JL, Canty John M, …, Mehta CR, Seltzer JH. Gaining Efficiency in Clinical Trials through the use of Cardiac Biomarkers. Journal of the American College of Cardiology, 2021; 77(15):1922-1933.
  16. Ankolekar S, Mehta CR, Mukherjee R, Smith J, Haddad T. Monte Carlo Simulation for Trial Design Tool. In: Piantadosi S, Meinert C. (eds) Principles and Practice of Clinical Trials. Springer, Cham, 2020. https://doi.org/10.1007/978-3-319-52677-5_251-1
  17. Ferreira JP, Rossignol P, Sharma, A, White WB, Mehta CR, Bakris G, Zannad, F. Multi-proteomic approach to predict specific cardiovascular events in patients with diabetes and myocardial infarction: findings from the EXAMINE trial. Clinical Research in Cardiology, 2020. Published on-line,  DOI 10.1007/s00392-020-01729-3.
  18. Elharram M, Sharma A, White W, Bakris G, Rossignol P, Mehta CR, Ferreira JP, Zannad, F. Timing of Randomization after an Acute Coronary Syndrome in Patients with Type-2 Diabetes Mellitus. American Heart Journal, 2020  (in Press).
  19. Ferreira JP, Mehta CR, Sharma A, Nissen SE, Rossignol P, Zannad F. Alogliptin after acute coronary syndrome in patients with type 2 diabetes: a renal function stratified analysis of the EXAMINE trial. BMC Medicine, 2020; 18:165.
  20. Ghosh P, Liu L, Mehta CR. Adaptive Multiarm Multistage Clinical Trials. Statistics in Medicine, 2020; 39:1084-1102.
  21. Hsiao S, Liu L, Mehta CR. Optimal Promising Zone Designs. Biometrical Journal, 2019; 61(5):1175-1186.
  22. Mehta CR, Liu L, Theuer C. An adaptive population enrichment phase III trial of TRC105 and pazopanib versus pazopanib alone inpatients with advanced angiosarcoma (TAPPAS trial). Annals of Oncology, 2018; 30(1):103:108.
  23. Menon V, Nichols SJ, …, Mehta CR, … Nissen SE. Fasiglifam-induced liver injury in patients with type-2 diabetes: results of a randomized controlled cardiovascular oucomes safety trial. Diabetes Care, 2018; 41:2608-2609.
  24. White WB, Jalil F, …, Mehta CR, Zannad F. Average Clinician-Measured Blood Pressures and Cardiovascular Outcomes in Patients with Type 2 Diabetes and Ischemic Heart Disease in the EXAMINE Trial. Journal of the American Heart Association, 2018; 7(20), e009114.
  25. Green SJ, Mentz RJ, …, Mehta CR, …O’Connor CM. Reassessing the Role of Surrogate Endpoints in Drug Development for Heart Failure. Circulation, 2018; 138(10):1039-1053.
  26. Cavender M, White WB, …, Cannon CP. Total Cardiovascular Events Analysis of the EXAMINE Trial in Patients with Type 2 Diabetes and Recent Acute Coronary Syndrome. Clinical Cardiology, 2018; vol 41, pages 1022-1027.
  27. Hwang Y-C, Morrow DA, …, Mehta CR, …, White WB. High-sensitivity C-reactive protein, low-density lipoprotein cholesterol and cardiovascular outcomes in patients with type 2 diabetes in the EXAMNE trial. Diabetes, Obesity and Metabolism, 2018; 20(3):654-659.
  28. Butler J, Hamo CE, …, Mehta CR,…, Anker DA, on behalf of the EMPEROR Trials Program. The potential role and rationale for treatment of heart failure with sodium-glocose co-transporter 2 inhibitors. European Journal of Heart Failure, 2017; 19(11):1390-1400.
  29. Marchenko O, Jiang Qi, Chuang-Stein C, Mehta CR, …, Soomin Park. Statistical Considerations for Cardiovascular Outcome Trials in Patients with Type 2 Diabetes Mellitus. Statistics in Biopharmaceutical Research, 2017; 9(4):347-360.
  30. Mehta CR. Commentary on Friedlin and Korn. Clinical Trials, 2017; vol 14(6), 605-608.
  31. Tamhane A, Gou J, Jennison C, Mehta CR, Curto T. A Gatekeeping Test on a Primary and a Secondary Endpoint in a Group Sequential Design with Multiple Interim Looks. Biometrics, 2017; 74(1):40-48.
  32. Liu L, Hsiao S, Mehta CR. Efficiency Considerations for Group Sequential Designs with Adaptive Unblinded Sample Size Re-assessment. Statistics in Biosciences, 2018; 10(2):405-419.
  33. Ghosh P, Liu L, Senchaudhuri P, Gao P, Mehta CR. Design and Monitoring of Multi-arm Multi-stage Clinical Trials. Biometrics 2017; 73:1289-1299.
  34. Cavender MA, White WB, …, Mehta CR, …Morrow DA. Serial Measurement of High Sensitivity Troponin 1 and Cardiovascular Outcomes in Patients with Type 2 Dibetes Mellitus in the EXAMINE Trial. Circulation, vol 135, issue 9, 2017.
  35. Heller SR, Bergenstal RM , …, Mehta CR, …Cannon CP. Relationship of glycated haemoglobin and reported hypoglycaemia to cardiovascular outcomes in patients with type 2 diabetes and recent acute coronary syndromes: the EXAMINE trial. Diabetes, Obesity and Metabolism, 2017; 1-8.
  36. Bhatt DL, Mehta CR. Adaptive Designs for Clinical Trials. New England Journal of Medicine, 2016; 375:65-74.
  37. White WB, Kupfer S, …, Mehta CR, …Cannon CP. Cardiovascular mortality in patients with type 2 diabetes and recent acute coronary syndromes. Diabetes Care 2016; 39:1267-1273.
  38. White WB, Wilson CA, …, Mehta CR, …Kupfer S. Angiotensin converting enzyme inhibitor use and major cardiovascular outcomes in type-2 diabetes treated with DPP-4 inhibitor alogliptin. Hypertension. 2016; 68(3):606-613.
  39. Mehta CR. Comment on “Some Challenges with Statistical Inference in Adaptive Design”. Journal of Biopharmaceutical Statistics, 2016; Vol 26, No 2, 402-404.
  40. Mehta CR, Liu L. An objective re-evaluation of adaptive sample size re-estimation: Commentary on ’25 years of Confirmatory adaptive designs’. Statistics in Medicine, 2016, 35, 350-358.
  41. Ravandi F, Ritchie EK, …, Mehta CR, Stuart RK, Kantarjian HM. Vosaroxin plus cytarabine versus placebo plus cytarabine in patients with first relapsed or refractory acute myeloid leukemia (VALOR): a randomized, controlled, double-blind, multinational, phase 3 study. Lancet Oncology, 2015; vol 16(9):1025-36.
  42. Zannad F, Cannon CP, Cushman WC, Bakris GL, Menon V, Perez AT, Fleck PR, Mehta CR, Kupfer S, Wilson C, Lam H, White WB. Heart failure and mortality outcomes in patients with type 2 diabetes taking alogliptin versus placebo in EXAMINE: a multicenter, randomized, double-blind trial. Lancet, 2015; vol 385, No 9982, p2067-2076.
  43. Léauté-Labrèze C, Hoeger P, …, Mehta CR, …  Voisard JJ. A randomized controlled trial of oral propranolol in infantile hemangioma. New England Journal of Medicine, 2015; vol 372, p735-746.
  44. Geiger MJ, Mehta CR, Turner RT, …, Gaydos B. Clinical development approaches and statistical methodologies to prospectively assess the cardiovascular risk of new antidiabetic therepies for type 2 diabetes. Therapeutic Innovation and Regulatory Science, 2015, vol 49(1) 50-64.
  45. Chaturvedi PR, Antonijevic Z, Mehta CR. Practical considerations for a two-stage confirmatory adaptive clinical trial design and its implementation:ADVENT Trial. Chapter 20, Practical Considerations for Adaptive Trial Design and Implementation, 2014. W. He et. al. (eds.), Springer Science, New York.
  46. Mehta CR, Schäfer H, Daniel H,   S. Biomarker driven population enrichment for adaptive oncology trials with time to event endpoints. Statistics in Medicine, 2014, 33, 4515-4531.
  47. Gewandter J, Dworkin RH, et. al., Mehta CR, et. al. Research designs for proof-of-concept chronic pain clinical trials: IMMPACT recommendations. Pain, 2014; volume 155, pages 1683-1695.
  48. Gao P, Liu L, Mehta CR. Adaptive sequential testing for multiple comparisons. Journal of Biopharmaceutical Statistics, 2014; volume 24, pages 1-24.
  49. Selker HO, Oye KA, Eichler H-G, Stockbridge N, Mehta CR, Kaitin K, McElwee N, Honig P, Erban JK, D’Agostino R. A proposal for integrated efficacy-to-effectiveness (E2E) clinical trials. Clinical Pharmacology & Therapeutics , 2014; volume 95, No. 2, pages 147-153.
  50. White WB, Cannon CP, Heller SK, Nissen SE, Bergenstal MD, Bakris GL, Perez AT, Fleck PR, Mehta CR, Kupfer, S, Wilson C, Cushman WC, Zannad F. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. New England Journal of Medicine, 2013; vol 369, No 14, pages 1327-35
  51. Gao P, Liu L, Mehta CR. Exact inference for adaptive group sequential designs. Statistics in Medicine, 2013; volume 32, 3991-4005.
  52. Gao P, Liu L, Mehta CR. Adaptive designs for non-inferiority trials. Biometrical Journal 2013, volume 55, 3, 310-321.
  53. Mehta CR. Adaptive clinical trial designs with pre-specified rules for modifying the sample size: a different perspective. Statistics in Medicine 2013, volume 32, No. 8, 1276.
  54. Mehta CR. Sample size reestimation for confirmatory clinical trials. Chapter 4, Designs for Clinical Trials: Perspectives on Current Issues, 2012. D. Harrignton (ed.), Springer, New York.
  55. Tamhane AC, Wu Y, Mehta CR. Adaptive extensions of a two-stage group sequential procedure for testing a primary and a secondary endpoint (II): Sample size re-estimation. Statistics in Medicine, 2012; 31(19):2041-54.
  56. Tamhane AC, Wu Y, Mehta CR. Adaptive extensions of a two-stage group sequential procedure for testing a primary and a secondary endpoint (I): Unknown correlation between endpoints. Statistics in Medicine 2012; 31(19):2027-40.
  57. Mehta CR, Pocock SJ. Response to Letter by E. Glimm on “Adaptive Increase in Sample Size when Interim Results are Promising”. Statistics in Medicine 2012; 31,  No. 1, 99.
  58. White WB, Bakris GL, Bergenstal RM, Cannon CP, Cushman WC, Fleck P, Heller S, Mehta CR, Nissen SE, Perez A, Zannad F. A cardiovascular safety study of the dipeptidyl peptidase 4 inhibitor Algoliptin, in type 2 diabetic patients with acute coronary syndrome: the EXAMINE trial. American Heart Journal 2011; 162: 620-626.
  59. Mehta CR, Gao P. Population enrichment designs: case study of a large multinational trial. Journal of Biopharmaceutical Statistics 2011; 21(4):831-45.
  60. Mehta CR, Pocock SJ. Response to Comments by Emerson, Levin and Emerson on “Adaptive Increase in Sample Size when Interim Results are Promising”. Statistics in Medicine 2012; 31:98-9.
  61. Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: a practical guide with examples. Statistics in Medicine 2011; 30: 3267-3284.
  62. Tamhane AC, Mehta CR, Liu L. Testing a primary and a secondary endpoint in a group sequential design. Biometrics  2010; 66, 1174-1184.
  63. Orloff J, Pinheiro J, Mehta CR, others with the McKinsey Trial Design Team. The Future of Drug Development: Clinical Trial Design. Nature Reviews Drug Discovery; on-line, 9 Oct. 2009.
  64. Brannath W, Mehta CR, Posch M. Exact confidence bounds for adaptive group sequential tests. Biometrics. 2009;  65, 539-546.
  65. Mehta CR, Gao P, Bhatt DL, Harrington RA, Skerjanec S, Ware JH. Statistical primer for cardiovascular research: optimizing trial design; sequential, adaptive and enrichment strategies. Circulation 2009; 119:597-605.
  66. Gao P, Ware JH, Mehta CR. Sample size re-estimation for adaptive sequential design. J.Biopharmaceutical Statistics 2008; 18 (6): 1184-96.
  67. Mehta AM, Mehta CR. Improving golf instruction with the iClub motion capture technology. Journal       of  Quantitative Analysis in Sports. 2008; volume 4, issue 2. (Berkeley Electronic Press.             http://www.bepress.com/jqas/vol4/iss2/12 )
  68. Mehta CR, Bauer P, Posch M, Brannath W. Repeated confidence intervals for adaptive group sequential trials. Statistics in Medicine 2008; 26, 5422-5433.
  69. Santner TJ, Pradhan V, Senchaudhuri P, Mehta CR, Tamhane A. Comparisons of confidence intervals for the difference of two independent binomial proportions. Computational Statistics and Data Analysis. 2007; 51, 5791-5799.
  70. Mehta CR, Jemiai Y. A consultant’s perspective on the regulatory hurdles to adaptive trials. Biometrical Journal. 2006; 4, 48, 598-603.
  71. Mehta CR, Patel NR. Adaptive, group sequential and decision theoretic approaches to sample size determination. Statistics in Medicine. 2006; 25, 3250-3269.
  72. Han KE, Catalano PJ, Senchaudhuri P, Mehta CR. Exact analysis of dose response for multiple correlated binary outcomes. Biometrics. 2004; 60, 216-224.
  73. Kim K, Tsiatis AA, Mehta CR. Computational issues in information-based group sequential clinical trials. Journal of the Japanese Society for Computational Statistics. 2003; 15.2, 153-167.
  74. Tsiatis AA, Mehta CR. On the inefficiency of the adaptive design for monitoring clinical trials. Biometrika. 2003; 90, 367-378.
  75. Rabbee N, Coull BA, Mehta CR, Patel NR, Senchaudhuri P. Power and sample size for ordered categorical data. Statistical Methods in Medical Research. 2003; 12, 73-84.
  76. Corcoran CD, Mehta CR. Exact level and power of permutation, bootstrap and asymptotic tests of trend. Journal of Modern Applied Statistical Methods. 2002; 1, 42-51.
  77. Mehta CR, Tsiatis AA. Flexible sample size considerations using information based interim monitoring. DIA Journal. 2001; 35(4):1095-1112.
  78. Corcoran CD, Mehta CR. Interval estimation for a binomial proportion. Comment. Statistical Science. 2001; 2:122-123.
  79. Corcoran CD, Mehta CR, Patel NR, Senchaudhuri P. Computational tools for conditional logistic regression. Stats in Med. 2001; 20:2723-2639.
  80. Corcoran CD, Mehta CR, Senchaudhuri P. Power comparisons for tests of trend in dose-response studies. Stats in Med. 2000; 19:3037-3050.
  81. Corcoran CD, Ryan L, Senchaudhuri P, Mehta CR, Patel NR, Molenbergs G. An exact trend test for correlated data. Biometrics. 2001; 57:931-948.
  82. Mehta CR, Patel NR, Senchaudhuri P. Efficient Monte Carlo methods for conditional logistic regression. Journal of the American Statistical Association. 2000; 95(449): 99-108.
  83. Mehta CR, Patel NR.  Exact permutational inference for categorical and nonparametric data. Statistical Strategies for Small-Sample Research. Hoyle R (ed).  Sage Publications (1999).
  84. Mehta CR, Patel NR, Senchaudhuri P.  Approximately exact inference for the common odds ratio in several 2 x 2 tables: Comment. Journal of the American Statistical Association. 1998; 93(444):1313-1316.
  85. Mehta CR, Patel NR, Senchaudhuri P.  Exact power and sample-size computations for the Cochran-Armitage trend test. Biometrics. 1998; 54:1615-1621.
  86. Mehta CR, Patel NR. Exact inference for categorical data. Encyclopedia of Biostatistics, vol 2, 1411-1422.  John Wiley & Sons (1998).
  87. Mehta CR. Discussion of paper on multiple testing by Wei and Glidden. Stats in Med. 1997; 16:848-849.
  88. Podgor MJ, Gastwirth JL, Mehta CR. Efficiency robust tests of independence in contingency tables with ordered classifications. Stats in Med. 1996; 15:2095-2105.
  89. Mehta CR, Patel NR. Exact logistic regression: theory & examples. Stats in Med. 1995; 14:2143-21
  90. Senchaudhuri P, Mehta CR, Patel NR. Estimating exact p-values by the method of control variates, or Monte Carlo rescue. Journal of the American Statistical Associations. 1995; 90(430):640-648.
  91. Mehta CR, Patel NR, Senchaudhuri P, Tsiatis AA.  Exact permutational tests for group‑sequential clinical trials.  Biometrics.  1994;  50, 1042-1053.
  92. Mehta CR, Hilton JF. Rejoinder to letter concerning “Exact power of conditional and unconditional tests:  Going beyond the 2×2 contingency table”.  American Statistician.  1994; 48, 2:175.
  93. Mehta CR. The exact analysis of contingency tables in medical research. Statistical Methods in Medical Research. 1994; 3,  2:135-156.
  94. Hilton J, Mehta CR, Patel NR. Exact Smirnov p‑values using a network algorithm. Computational Statistics and Data Analysis.  1994; 17, 4, 351-361.
  95. Hilton J, Mehta CR.  Power and sample size calculations for exact conditional tests with ordered categorical data.  Biometrics. 1993; 49, 609-616.
  96. Mehta CR and Hilton JF.  Exact power of conditional and unconditional tests:  Going beyond the 2×2 contingency table.  American Statistician.  1993; 47:91-98.
  97. Agresti AB, Lang JB, Mehta CR.  Some empirical comparisons of exact, modified exact, and higher order asymptotic tests of independence for ordered categorical variables.  Communications in Statistics:  Simulation and Computation. 1993; 22(1).
  98. Mehta CR, Walsh S. Rejoinder to letter concerning “Comparison of exact, mid-p and Mantel-Haenszel confidence intervals for the common odds ratio across several 2×2 tables”.  American Statistician. 1992; 47, 1, 86-87.
  99. Strawderman RL, Mehta CR. On the validation of exact tests for nonparametric inference.  Computational Statistics and Data Analysis.  1992; 14:263-266.
  100. Mehta CR. Comment: An interdisciplinary approach to exact inference for contingency tables.  Statistical Science.  1992; 7:167-170.
  101. Mehta CR, Patel NR, and Senchaudhuri P. Exact stratified linear rank tests for ordered categorical and binary data.  Journal of Computational and Graphical Statistics, 1992; 1:21-40.
  102. Mehta CR.  StatXact 2:  A statistical package for exact nonparametric inference.  The American Statistician.  1991; 45(1):78-79.
  103. Mehta CR. Action and Contemplation. Prabuddha Bharata, Volume 96, April, 1991.
  104. Mehta CR, Walsh SJ.  Comparison of exact, mid-p and Mantel-Haenszel confidence intervals for the common odds ratio across several 2×2 tables.  American Statistician . 1992; 46(2):146-150.
  105. Mehta CR.  StatXact: A Statistical Package for Exact Nonparametric Inference.  Journal of Classification.  1990; 7:111-114.
  106. Agresti A, Mehta CR, Patel NR.  Exact inference for contingency tables with ordered categories.  Journal of the American Statistical Association. 1990; 85(410):453-458.
  107. Wei LJ, Smythe R, Mehta CR.  Interval estimation with restricted randomization rules.  Biometrika.  1989; 76(2):363-368.
  108. Hirji K, Tsiatis A, Mehta CR.  Median unbiased estimation for binary data.  American Statistician.  1989; 43:7-11.
  109. Mehta CR, Patel NR, Senchaudhuri P.  Importance sampling for estimating exact probabilities in permutational inference.  Journal of the American Statistical Association.  1988; 83(404):999-1005.
  110. Hirji K, Mehta CR, Patel NR.  Exact inference for matched case‑control studies.  Biometrics. 1988; 44(3):803‑814.
  111. Mehta CR, Patel NR, Wei LJ.  Constructing exact significance tests with restricted randomization rules.  Biometrika.  1988; 75(2):295‑302.
  112. Hirji K, Mehta CR, Patel NR.  Computing distributions for exact logistic regression.  Journal of the American Statistical Association.  1987; 82(400):1110‑1117.
  113. Cassileth BR, Knuiman MW, Abeloff MD, Mehta CR.  Anxiety levels in patients randomized to adjuvant therapy versus observation for early breast cancer. J Clin Oncol.  1986; 4(6):972‑74.
  114. Mehta CR, Patel NR.  FEXACT: A Fortran Subroutine for Fisher’s exact test on unordered r x c contingency tables.  ACM Transactions on Mathematical Software.    1986; 12(2):154‑161.
  115. Gupta PC, Mehta CR, Pindborg JJ.  Intervention of tobacco chewing and smoking habits.  Am J Pub Heal (letter).  1986; 76(6):709.
  116. Gupta PC, Mehta CR.  An intervention study of tobacco chewing and smoking habits for primary prevention of oral cancer among 12,212 Indian Villages.  Tobacco: A Major International Health Hazard.  IARC Sci Publ. No. 74, 1986.
  117. Mehta CR, Patel NR.  A hybrid algorithm for Fisher’s exact test in unordered r x c contingency tables.  Communic in Statist.  1986; A15: 2.
  118. Mehta CR, Patel NR, Gray R.  On computing an exact confidence interval for the common odds ratio in several 2 x 2 contingency tables. Journal of the American Statistical Association. 1985;  80(392):969‑973.
  119. Gelber R, Lavin P, Mehta CR, Schoenfeld D.  Acute and Chronic Toxicity Testing.   In: Rand GM, Petrocelli SR, eds.  Fundamentals of Aquatic Toxicology.  Washington: Hemisphere Publishing Corporation, 1985:110‑123.
  120. World Health Organization Group Report.  Control of Oral Cancer in Developing Countries.  Bull WHO. 1984; 62(6):817‑830.
  121. Mehta CR, Patel NR, Tsiatis A.  Exact significance testing to establish treatment equivalence ordered categorical data.  Biometrics.  1984; 40:819‑825.
  122. Mehta CR, Cain KC.  Charts for the early stopping of Pilot Studies.  J Clin Oncol.  1984; 2(6):676‑682.
  123. Tsiatis  AA, Rosner GL, Mehta CR.  Exact confidence intervals following a group sequential test.  Biometrics.  1984; 40:797‑803.
  124. Mehta CR, Patel NR.  A  network  algorithm  for performing  Fisher’s exact test in rxc contingency tables.  Journal of the American Statistical Association. 1983; 78(382)427‑434.
  125. Mehta CR, Beranek WM.  Estimating asset volatility by means of a Bayesian switching regression.  J Financial and Quantitative Analysis.  1982; 17(2)241‑263.
  126. Mehta CR.  Sequential comparison of two exponential distributions with censored survival data.  Biometrika.  1981; 68(3):669‑675.
  127. Ruckdeschel JC, Mehta CR, Salazar OM, Cohen M, Vogl S, Koons LS, Lerner H. Chemotherapy of inoperable, non‑small cell bronchogenic carcinoma. EST 2575,  Generation  II.  Cancer Treat Rep.  1981; 65(11‑12):965‑971.
  128. Ruckdeschel JC, Mehta CR, Salazar O, Creech R, Sponzo RW. Chemotherapy of metastatic non‑small cell bronchogenic carcinoma: EST 2575 Generation III, HAM vs CAMP.  Cancer Treat Rep.  1981; 65(11‑12):959‑963.
  129. Kilton LJ, Bradley M, Mehta CR, Livingston DM.  A rapid and sensitive quantitative immunoassay for the large SV40 T Antigen.   J Virol.  1981; 38(2):612‑620.
  130. Creech RH, Mehta CR, Cohen M.  Phase II Master Protocol for evaluation of new chemotherapeutic regimens in patients with inoperable non‑small cell lung carcinoma  (EST 2575,  Generation  I).   Cancer Treat Rep.  1981; 65(5‑6):431‑438.
  131. Mehta CR, Patel NR.   A network algorithm for the exact treatment of the 2xk contingency table.  Commun in Statist.  1980; B9(6):649‑664.
  132. Vincent RG, Mehta CR, Sealy R.  Chemotherapy of extensive large cell and adenocarcinoma of the lung.  A randomized trial in 210 patients.  Cancer. 1980; 46(2):256‑260.
  133. Browman GP, Gorka C, Mehta CR, Lazarus H, Abelson HT.  Studies with a DDMP‑resistant L1210 leukemia cell line without cross‑resistance to Methotrexate.  Biochem Pharmac.  1980; 29:2241‑2245.
  134. Begg CB, Mehta CR.  Sequential analysis of comparative clinical trials.  Biometrika.  1979; 66(1):97‑103.
  135. Vogl S, Mehta CR, Cohen M.   MACC chemotherapy for adenocarcinoma and epidermoid carcinoma of the lung.  Cancer.  1979; 44(3):864‑868.
  136. DeWolf WC, Carroll PG, Mehta CR, Martin SL, Yunis EJ.  The genetics of PLT response II:  HLA‑DRW is a major stimulating determinant.  J Immunol. 1979; 123(1):37‑42.
  137. McDonald JA, Li FP, Mehta CR.  Cancer mortality among beekeepers.  J Occupat Med.  1979; 21(12):811‑813.
  138. Carroll PG, DeWolf WC, Mehta CR, Rohan J, Yunis EJ.  Centroid cluster analysis of the primed lymphocyte test.  Transpl. Proceedings.  1979; 11(4):1809.
  139. Mehta CR.  Optimal strategies for raising the wellhead price of natural gas.  J Appl Cybern Mgmt Sci.  1975; 14(1&2)::40‑63.
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