Cytel is the largest provider of statistical software and advanced analytics for clinical trial design and execution. For over thirty years, Cytel’s scientific rigor and operational excellence have enabled biotech and pharmaceutical companies to navigate uncertainty, prove value and make confident, evidence-based decisions. Our experts deliver industry-leading software, data-driven analytics, real-world evidence and strategic consulting. Headquartered in Waltham, Massachusetts, Cytel has more than 1,400 employees across North America, Europe and Asia. For more information about Cytel, please visit us at www.cytel.com. You also can follow us on LinkedIn and Twitter and like us on Facebook.


Cytel brings its expertise in adaptive designs to the design and implementation of the industry’s most innovative clinical trials. These highly flexible trials raise peculiar challenges for enrollment, data collection and regulatory submission. Why not have the industry leaders in adaptive design working on your adaptive trials?

Cytel’s dedicated team of professionals is here to help you address an array of challenges when conducting drug development. Whether you face a complex statistical issue or the need for knowledgeable collaborators to handle biometrics and trial implementation, Cytel has skilled professionals available when you need them.

Cytel data scientists apply advanced statistical techniques including predictive modeling of biological processes and drug interactions to unlock the potential of big data. Our team supports biomarker discovery and diagnostic test development based on biomedical signals and images, and real world evidence analysis.

Exposure (and Dose) Response Analyses, including pharmacokinetic and pharmacodynamics analyses, guide critical decisions in drug development. Cytel’s expert Quantitative Pharmacology and Pharmacometrics group delivers high quality solutions to help our customers get those decisions right.

Cytel has a strong track record in providing CDISC solutions and we are a CDISC Registered Solutions Provider. We have converted more than 150 studies as part of NDA submissions, all of which have been accepted by the FDA. The Clinical Data Interchange Standards Consortium (CDISC) devised the SDTM and ADaM models to standardize data structures for submission of raw and analysis data from clinical trials. These standard formats are increasingly expected by regulatory authorities including the FDA.

Extensive time and energy is often spent designing feasible clinical studies. East's broad range of statistical tools and expert simulations provides an invaluable resource for the rapid construction of trial designs. Illuminating graphs and charts allow for accessible comparison, while a user-friendly interface encourages exploration and experiment.

EnForeSys is a user-friendly decision tool that leverages simulation methods to predict recruitment milestones with high accuracy. Armed with a reliable probability of success, you can rest assured that your trial will reach its targeted enrollment on time and on budget.

We believe that expert statistical input has the power to shape the future of clinical development: de-risking portfolios, accelerating timelines, and increasing the probability of success.



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Waltham, MA 02451


Contact: Rebecca Grimm


  • Cytel provides an overview of how Scoring Functions can be implemented for enhanced clinical trial selection.

  • When Cyrus Mehta introduced the Promising Zone Design over a decade ago, the new statistical method not only transformed the allocation of scarce resources within a clinical trial setting but also the design reconceptualized how sponsors could increase investment in their trials. Read about the Promising Zone Design in the available blog post.

  • Cohort-based enrollment can thus slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This type of cohort-based designs can also be inefficient, especially if the trial needs to be frequently suspended. Read how to shorten the study duration of phase I trials and reduce the number of accrual suspensions with the use of rolling-enrollment designs is recommended which allows concurrent patient enrollment that is faster than cohort-base enrollment.

  • Program and portfolio optimization creates a framework throughout the course of the clinical development journey, that enables trial sponsors to make decisions about when to continue a clinical trial. Learn where Bayesian methods should be injected into the process of optimization at the program and portfolio level.

  • In recent years, platform clinical trials have gained substantial support and provide an efficient way of testing multiple compounds in a single and consistent framework. In this presentation we review platform trials including terminology, protocol structure overview and discuss some of the key advantages. We also share a concept for a platform trial that utilizes Bayesian modeling to make Go/No-Go decisions and discuss the potential benefits and risks of a platform trial.  

  • For many decades the Pareto Frontier has been employed by actors in the private sector to evaluate and understand the benefits of various strategic options. When trying to understand the risks and benefits of a clinical strategy, Cytel researchers urge using a modified version of this process, built primarily with clinical development in mind. In this blog learn more about using the Pareto concept in clinical strategy and decision-making.

  • When selecting clinical trial designs, how many design options should a sponsor explore? Would a sponsor feel more confident having an optimized design out of ten potential ones, or simulating several million designs to discover that there are three or four better options? Cytel’s experience suggests that this depends very much on the specific sponsor and the specific conditions of the clinical trial. Here are five considerations for sponsors when making this complex decision.

  • The urgent need to discover and assess the efficacy and safety of COVID-19 vaccine candidates will affect the future clinical development of all infectious disease vaccine candidates, including those for diseases like tuberculosis. This blog reviews the design features of these clinical trials, and why Bayesian methods reduced risk and generated scientific findings more quickly.