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Prioritizing Multiple Endpoints in Adaptive Clinical Trials with New Graph-Based Methods

Prioritizing Multiple Endpoints in Adaptive Clinical Trials with New Graph-Based Methods


June 23, 2026
Cyrus Mehta, co-founder of Cytel
Cyrus Mehta

Over twenty years ago, the “adaptive” trial idea began — clinical trials that could be modified or stopped for efficacy or futility mid-trial at interim looks — and from there have only become more innovative as new adaptations have been developed.

Cyrus Mehta, Cytel’s co-founder and a leading expert on adaptive trial designs, has recently published his new paper, “Graph Based, Adaptive, Multiarm, Multiple Endpoint, Two-Stage Designs,” with co-authors Ajoy Mukhopadhyay and Martin Posch. In it, he describes the latest advancement to adaptive trial designs.

In this interview, Cyrus discusses this new methodology, the practical and regulatory implications for trialists, and what’s on the horizon for adaptive designs.

 

You’ve recently published a new paper on adaptive designs — what led to this most recent development?

To answer that question, you have to look back to the development of clinical trials. In the previous century, clinical trials were just single-look trials — once the study was over, you’d open the box and see whether the trial was successful or a failure. Many trials failed and a lot of patients were put through unnecessary treatments.

The first development to improve that was the group sequential design. Here you could look at the data at regular intervals and either stop the study for efficacy or futility — but that was all you could do.

Then, two decades ago, began the adaptive trial idea — not only could you stop a trial early for efficacy or futility, but you could also change the design itself. This was a major breakthrough, and what really got my attention. We were the first to implement this in our East software in the early 2000s, and from there developed a consulting practice around adaptive designs.

The adaptive changes we considered included changing the sample size (sample-size re-estimation designs, including “promising zone” designs), comparing multiple treatments to a common control with sample size re-estimation (multi-arm multi-stage adaptive designs), and dropping arms at the interim analysis (seamless phase II/III designs).

Finally, we are designing adaptive trials that involve both multiple treatments as well as multiple endpoints. For example, adaptive oncology trials that test the treatment effect for both progression-free survival and overall survival; cardiology trials that test the treatment effect for stroke, myocardial infarction, and heart failure hospitalization; and population enrichment trials that ask about the treatment effect in the full population as well as in a biomarker sub-group. So, the question is, what is the most efficient way to design trials that compare multiple treatments for multiple endpoints and multiple subgroups?

That finally leads to the current paper and our latest innovation: putting priorities on or assigning weights to different hypotheses with graph-based methods.

 

What are graph-based, adaptive, multi-arm, multiple endpoint, two-stage designs?

When you are testing multiple hypotheses involving multiple treatments, multiple endpoints, multiple populations, and combinations of these, it is desirable to assign priorities to the different hypotheses  by giving more weight to those hypotheses that have greater clinical benefit or are of greater interest than others. But the question arises: how do you assign weights to different hypotheses?

This is where we use the graph-based methods — an intuitive way of prioritizing which endpoint or hypothesis is more important than another. A graph consists of weighted nodes representing the various hypotheses to be tested and directed edges for propagating the weights from rejected hypotheses to ones yet to be tested. It is an intuitive and convenient way to conceptualize and display the study team’s strategy for testing the various hypotheses in a hierarchical manner, while recognizing that some hypotheses are more important than others.

Graph-based methodologies were originally only developed for trials without an interim analysis, that is, no adaptation, just simple graph-based weighting of multiple endpoints. We made it possible to take interim looks and drop hypotheses that are not worth further investigation, change the sample sizes of hypotheses that are good, add new hypotheses and, at the same time, have these graph-based weighting strategies.

So, in a nutshell, it is the most flexible way you could design a clinical trial.

 

In the paper, you discuss the use of this design in a schizophrenia trial. What hypotheses were tested?

In this case, the sponsor was developing a treatment for schizophrenia, comparing a common control to a high-dose arm and a low-dose arm. The trial also had two endpoints: the positive and negative symptom score for everybody, and the subset of the population that had negative symptoms. The sponsor wanted to know whether they could make a claim in the product label for the whole population, not just for the negative symptom subset.

 

What are the practical and regulatory implications for trialists going forward?

Things change slowly in our business. For example, group sequential methods were available by the middle of 1970s, but nobody wanted to be the first to use them — adoption was very gradual, for this and other designs. Even after the first guidance document for adaptive designs was released in 2010, regulatory agencies said that these were less well understood methods, which was an inhibition right away.

The designs I discuss here fall under the category of complex innovative designs, which are gradually being adopted. But I suspect that not all of the flexibility that we are offering will be taken up right away. People will start slowly, perhaps with multiple doses (which some are already doing) and then maybe with one additional endpoint, then maybe two, and so on, adding things a little at a time.

But the important point is that these designs are applicable to confirmatory or Phase III trials. That means they have to meet a very strict statistical standard known as strong control of family wise error rate and we’ve demonstrated through peer review that these methods do have strong family wise error rate control. So that’s the implication for the future.

 

What questions are still to be answered with these methods?

Well, I’ve only focused on the frequentist paradigm for trial design. That is the one that people are most familiar with, most people use, and that has the best chance of meeting FDA approval.

In terms of frequentist methods, one unanswered question that we are working on right now is estimating the treatment effect. So, what we have is a hypothesis testing. We’re asking a question, does this drug work or not? It’s a yes or no question. But people want to know, OK, now you’ve shown that it works. How much better is it than the control? What is the treatment effect? With simple designs, we’ve worked out unbiased treatment effects and appropriate confidence intervals for the treatment effect. But when your trial has multiple questions, multiple endpoints, multiple treatments, it’s still an open question on how to estimate the parameters in a way that is consistent with the hypothesis testing. There’s some progress, but that is that is an open question for the future.

 

Lastly, what’s on the horizon for adaptive designs?

For this class of problems, people are looking at many other types of methods, including the use of AI and machine-learning techniques, Bayesian methods, causal inference, and the use of real-world data. These are areas of great interest in the field, and an exciting future for adaptive designs.

 

Interested in learning more?

Read the full article, “Graph Based, Adaptive, Multiarm, Multiple Endpoint, Two-Stage Designs,” published in Statistics in Medicine:

Read the full article
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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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