Monday, July 28, 2014

CROS NT Releases Autumn 2014 CROS Academy Course Schedule

CROS NT is pleased to announce our course and webinar schedule for CROS Academy for this autumn. We are bringing back our classic course list and adding one new city to our schedule.

CROS Academy also offers in-house, tailored trainings for companies who have specific training needs or want to provide training for a large group of people.

CROS Academy Autumn/Fall 2014 Schedule

23 September 2014 | Webinar
Statistical Considerations in Oncology Part I: Requirements for Successful Phase I and II Studies

Oncology is a main area of focus for many pharmaceutical and biotechnology companies around the world. Cancer claims more than 7 million lives a year and has over 450 indications. This two-part webinar describes why oncology is a unique area and the statistical methods best suited for these trials.

Part I covers Phase I and II product development which lays the foundation for successful Phase III studies. Particularly in oncology, the heterogeneity of the disease can influence the planning of clinical trials.

Register Here

30 September 2014 | Webinar
Statistical Considerations in Oncology Part II: Statistical Methods for Early Decisions in Phases II and III

During Phase III development, efficacy of the drug should be confirmed. Many new products have to find a very specific mechanism of effectiveness, so therefore subpopulations are necessary. Flexible study designs that allow early termination and the use of fewer patients can be a successful solution for determining clinical efficacy. The advantages and disadvantages of flexible designs will be discussed in Part II. 

Register Here

9 October 2014 | Copenhagen, Denmark | Course
Introduction to Adaptive Trial Design

Adaptive Trial Designs offer greater flexibility and the possibility to modify a study in progress based on new information derived from accumulated data. Participants will learn the adaptations possible in a trial, the potential risks and benefits of adaptive trials and conduct a hands-on exercise.

Course brochure and registration link coming soon.

28 October 2014 | Paris, France | Course
Understanding the Statistical Elements of a Study Protocol - for Non-Statisticians

This course is offered to clinical trial professionals who wish to comprehend the roles of statistics and statisticians in clinical trials. The course aim is to enhance one's understanding of basic statistical principles as well as the statistician's view on clinical trial aspects.

Course brochure and registration link coming soon.

29 October 2014 | Paris, France | Course
Introduction to Adaptive Trial Design 

See description above. A discount is offered to participants who register for both Paris courses.
Course brochure and registration link coming soon.

12 November 2014 | London, UK | Course
Understanding the Statistical Elements of a Study Protocol - for Non-Statisticians

Course brochure and registration link coming soon.

13 November 2014 | London, UK | Course
Introduction to Adaptive Trial Design 

See description above. A discount is offered to participants who register for both London courses.
Course brochure and registration link coming soon.

Tuesday, July 22, 2014

Strategies for Reducing Clinical Data Management Costs Without Sacrificing Quality

Clinical Data Management has certainly evolved over the years to reflect the changing clinical trials landscape. Pharmaceutical, biotechnology and medical device companies rely on various outsourcing methods to carry out data management activities such as putting a Functional Service Provider (FSP) team in place for product-specific work, outsourcing to low-cost countries or centralizing biometrics to ensure quality and traceability. With the advancement of technology, there are also various data collection, reporting and visualization systems to consider. Sponsors need to find cost-effective ways to manage vast amounts of data in a traceable and transparent way

The key to any successful project is effective planning. Due to increased requirements by regulatory authorities and new legislation, such as the EMA's upcoming clinical data transparency law, the Clinical Data Manager has more of a stake in the requirements for data collection, data accuracy and quality.

Moreover, when it comes to your clinical data management activities, how do you evaluate quality vs. cost? CROS NT discusses cost reduction strategies for clinical data management that still ensure top quality clinical data.

Consulting a CRO with Global Libraries. The use of a Global Library can significantly reduce DM costs by re-using forms - cost reductions of up to 50% if implementing the centralized biometrics outsourcing model. In EDC studies, Sponsors can save 40-45% on second and subsequent studies in database setup, reuse of forms from the library and training costs.

This method guarantees a uniform traceability of data, better preparation for the EMA's transparency initiative, due diligence ready datasets and documentation and one single Project Manager.

CDISC Implementation. Adopting and implementing CDISC standards can increase drug development analysis costs. However, identifying the right process for implementation and choosing the appropriate technology can reduce the costs of adopting these standards, making it a very worthwhile investment in the medium to long term. Conversion to CDISC standards generally includes SDTM (Study Data Tabulation Model), ADaM (Analysis Data Model) and CDASH (Clinical Data Acquisition Standards Harmonization). 

It is important to start implementing CDISC standards during data collection by having Data Managers design the CRF using CDASH standards. This makes the process of mapping data into SDTM and ADaM datasets much more straightforward. 

Consult your Data Manager on EDC. eCRF significantly reduces the time and costs for certain stages of a clinical study. Direct and indirect costs can be reduced thanks to parallel operations during the use of a centralized database that can be accessed simultaneously by many people using the Internet. The availability of cost-effective, open source or proprietary EDC applications for individual physicians/researchers, departments and institutions has the potential to improve clinical and research activities. Cost is an important, if not the most important, factor to be considered while choosing an EDC system. Although the nature of licensing and support required to maintain EDC systems are the main predictors of cost, data capture and workflow at the site implementation also have a major influence.

Introducing FSP 2.0 - If the FSP model is your outsourcing method of choice, consider a new and improved model that deals with the negative effects regarding staff retention and dedication. CROS NT's "next-generation FSP" solution can put together a DM team that controls costs by significantly lowering staff turnover, guaranteeing coverage of sick days and holidays and eliminating the "silo" effect.

If you'd like to evaluate your upcoming data management needs, schedule a meeting with CROS NT to decide which outsourcing strategy is best and how we can reduce DM costs. CROS NT has over 20 years of clinical data management experience, managing more than 1,000 studies with resources worldwide. We are also a CDISC Gold Member and can consult on the implementation of these data standards. 

Tuesday, July 1, 2014

The Approaches of Successful Sample Size Calculation

In an extract from our CROS Academy course: "Understanding Study Designs & Successful Sample Size Calculation", we discuss the different approaches to sample size calculation and why it is an important statistical component in successful clinical trial methodology and strategy.

The target of drug development is to license a drug for a particular disease which is beneficial to all patients and bears a minimal risk on a patient's health. That being said, the drug development cycle consists of a pre-clinical phase followed by Phases I through IV, and each phase has its own population characteristics and defined endpoints. The target populations are as follows:
  • Pre-clinical Phase: vitro- and animal testing to test for kinetics, toxicity and carcinogenicity
  • Phase I: Phase Ia looks for healthy volunteers, while Phase Ib looks for diseased patients with a sample size of approximately less than 10 per dose level to determine the highest possible dose with an "acceptable" rate of dose-limiting toxicity
  • Phase II: Restricted diseased patient population with a sample size of approximately 30-100 per dose level to examine the preliminary evidence of efficacy
  • Phase III: Unrestricted diseased patient population with a sample size of over 100 in order to confirm efficacy
  • Phase IV/Post-market: Unrestricted diseased patient population with a wide variety of medical conditions; with a sample size of usually between 300-10,000 to monitor the safety of a drug once it is released on the market.
There are two approaches to sample size calculation: the precision-based approach and the power-based approach. 

The precision-based approach estimates an unknown parameter with a certain precision. This approach limits the confidence interval of the parameter to a certain width. 

In the power-based approach, the target is to reject a Null-hypothesis with certain error probabilities. This approach is related to hypothesis testing. The necessary assumptions provided include the level of significance and power, standard deviation, clinically relevant significance and the effect size. 

The power-based approach is also used for time-to-event data such as "survival" time (time) or "failure" and "censored" (event). In the case of time-to-event data, the Kaplan-Meier method estimates survival time and log-rank-test can be used where the sample size is calculated in two steps: determining the number of events needed, and then determining the number of patients needed.

It should be noted that the following practical considerations have an effect on sample size:
  • Type I and Type II error
  • Variability of data
  • Effect size
The determination of sample size is critical in the planning and success of clinical trials, however sample size formulas exist for many situations. In complex situations, sample size can also be determined by simulation. Sample size calculation is always based on assumptions about real effect sizes and variation of the data, therefore it is important to get as realistic assumptions as possible. CROS NT biostatisticians recommend calculating sample size under different assumptions as well as in a conservative manner.

CROS Academy Biostatistics Courses
CROS Academy offers biostatistics courses taught by our expert biostatisticians. We organize courses periodically in various cities throughout Europe or the U.S., however we also offer in-house, tailored courses based on company requests. Our current list of courses includes:
  • Understanding Study Designs & Successful Sample Size Estimation
  • Understanding the Statistical Elements of a Study Protocol - for Non-Statisticians
  • Introduction to Adaptive Trial Design
  • Statistical Considerations for Oncology Trials (Early and Late Phases)

Tuesday, June 24, 2014

The Advantages and Disadvantages of Endpoints in Oncology Trials

In this week's blog post, we look at an extract from an article written by CROS NT's expert biostatistician, Thomas Zwingers, on adaptive trial design for oncology studies. How can biostatisticians use adaptive trial designs to deal with the advantages and disadvantages of endpoints in oncology studies?

Oncology is significantly different than other therapeutic areas. One contributing factor is the long timelines to reach clinical endpoints. The ultimate endpoint for registration of a new drug is still the "Overall Survival Time". Surrogate endpoints like "response rates" or "time to progression" are often used but mostly as secondary endpoints or in earlier phases of the development process.

Let's look at the advantages and disadvantages of these endpoints:
  • Overall Survival: the advantages are that is it universally accepted as a direct measure of benefit and easily measured, however its disadvantages are that it usually involves larger studies, may be affected by crossover therapy and sequential therapy and includes non-cancer deaths.
  • Disease-Free Survival or Progression-Free Survival: The advantage to this endpoint is that it usually requires a smaller sample size and shorter follow-up compared to survival studies. However, this endpoint is not statistically validated as a surrogate for survival in all settings, and definitions may vary among studies.
  • Time to Progression: The advantages to TTP endpoints are that they usually require a smaller sample size and shorter follow-up and they are not affected by crossover or subsequent therapies. Like the above-mentioned endpoint, TTP is not statistically validated as a surrogate for survival. Additionally, it is not precisely measured and is subject to assessment bias particularly in open-label studies. Definitions may also vary among studies.
The long time period before any conclusion from the study can be drawn is the main reason for the use of adaptive designs in the this setting. Adaptive designs enable researchers to make early decisions on interim analyses on one or more of the following issues:
  • Early stopping
  • Adjustment of sample size
  • Treatment selection
  • Change of observation time
  • Change of test statistic
In Phase III trials, or even Phase IIb trials, the main accepted endpoints usually need a long time before they can be evaluated. Implementing a Seamless Phase II/III adaptive design can minimize time and save costs in terms of patient enrollment. 

To request the full article "Adaptive Trials in Oncology: Part III Adaptive Designs for Successful Late Phase Oncology Trials", or to request the full article series, send an email to

Thursday, June 12, 2014

Time to Upgrade to FSP 2.0 - Visit CROS NT at DIA in San Diego

Is your company currently using the Functional Service Provider (FSP) model? 

If so, it's time to upgrade to FSP 2.0. If you are attending the DIA 50th Annual Meeting this year in San Diego, CROS NT's CEO, Andrew MacGarvey, will be there at Booth #1810 discussing FSP 2.0. This "next-generation FSP" retains the advantages needed by Sponsors while mitigating the disadvantages of the old model such as staff turnover and avoiding the "silo" approach. Andrew will reveal FSP at the CROS NT booth during the show.

What else can you expect from CROS NT at this year's show?

1. Learn about EU data transparency laws and how to prepare: If you are conducting trials in Europe, come by our booth to talk with our experts on clinical data transparency in Europe. A recent initiative by the European Medicines Agency on data transparency was finally passed into draft law by the European Union, requiring that detailed summaries of clinical trials are published in a publicly accessible database once marketing authorization is granted. Our Global Head of Business Development, Chris Hamilton, published an article on this topic last month and can provide consultancy on how to prepare your data in a traceable and transparent way.

2. Contribute to a great cause: At this year's DIA Annual Meeting in San Diego, CROS NT is dedicating some of its time at the conference to raising money for the Make a Wish Foundation. CROS NT chose this organization since it greatly benefits the lives of children diagnosed with a life-threatening medical condition. The Make a Wish Foundation grants a wish, on average, every 38 seconds, and these wishes can mark a turning point in a child's battle with an illness.

Come by the CROS NT booth and simply let CROS NT scan your badge. For every badge scanned, CROS NT will donate $3 USD to the Make a Wish Foundation. We will announce the total amount raised on our CROS in the Community page after the conference.

3. DATATRAK Partner Scavenger Hunt: CROS NT, as part of the DATATRAK Partner Connect Program, is participating in its DIA Scavenger Hunt. Come to our booth to get an answer to this scavenger hunt game for your chance to win an iPad Mini. 

Attending the DIA Annual Meeting from CROS NT will be CEO, Andrew MacGarvey, Chairman, Paolo Morelli, Business Development Managers John Bancroft and Jeff Welch. Be sure to stop by our booth or schedule a meeting. 

Friday, June 6, 2014

Making Effective Use of Adaptive Designs in Early Oncology Trials

Adaptive Designs provide greater efficiency and flexibility for Investigators/pharmaceutical companies by minimizing the number of patients in each development phase and reducing the time needed for the whole process. In this week's blog post, Principal Biostatistician, Thomas Zwingers, discusses some of the designs used in Phase I and II.

Study design in early phases is extremely important. Safety and efficacy, ethical considerations and long patient recruitment all need to be taken into account. Patient recruitment retention is challenging, and biostatisticians should be involved in the beginning to define protocol requirements. There will generally be vast amounts of data to analyze, and  therefore the biostatistician will need access to real-time data in order to make go/no-go decisions. Investing time in the proper design set-up of an oncology trial in the early phases is essential to increase success rates in later phases.

In Phase I trials, the 3+3 design is the most commonly used design to evaluate the highest dosage with acceptable toxicity. But this design can be very insufficient in terms of number of patients and/or precision of the dosage. Bayesian designs like the Continuous Reassessment Method and its modifications offer the chance to reach the Dose Limiting Toxicity (DLT) as well as the advantage of treating more patients with a possibly effective dosage.

While the 3+3 design can be evaluated by physicians themselves, the CRM Methods need the support of a statistical program, which might be a drawback.

The goal of Phase II development is to examine the preliminary evidence of efficacy of a new compound and exclude compounds found to be ineffective from further research. Adaptive designs, like the Simon's 2-stage design, have been used for a long time in Phase II.

A patient's hetergeneity limits the evidence of study results. To overcome this problem, Randomized design methods are used more frequently whenever the number of patients available allow. Randomized "Parallel Non-comparative Regimens" compare different treatment regimens, characterized either by different dosages or different application intervals, in an effort to avoid various types of bias, including patient selection bias.

In contrast to other indications where Phase I trials are often conducted with healthy volunteers and patients with the targeted disease are only involved in Phase II, in Oncology diseased patients are already involved in Phase I. Flexible 2-stage designs have been proposed (Seamless Phase I/II trials) which can minimize the number of patients needed to come to a "go/no-go" decision.

This blog is an extract from Part II in Thomas Zwinger's 4-part series on "Adaptive Trials in Oncology". If you would like the full article or would like to sign up to receive the full series, please email us at

Wednesday, May 28, 2014

Ask the Experts: Late Phase Oncology Trial Design

On the 6th of June, Principal Biostatistician Thomas Zwingers is hosting Part II of his German-language webinar "Statistical Considerations in Oncology" (Statistiche Überlegungen für klinische Studien in der Onkologie). Part II focuses on statistical methods for early decisions in Phases II and III. 

According to CROS NT's expert biostatistician, how can adaptive trial design be a method for the future of cancer research?

During Phase III development, efficacy of the drug should be confirmed. Many new products have to find a very specific mechanism of effectiveness, so therefore subpopulations are necessary. Flexible study designs that allow for early termination and the use of fewer patients can be a successful solution for determining clinical efficacy. In oncology trials, we need to take into consideration differentiating factors such slow patient recruitment, the importance of patient genotype, the use of various treatment combinations and that the primary endpoint is overall survival time or time to progression.

Adaptive Designs in late phase cancer research offer the possibility to make modifications to a study while in progress on the basis of new information pulled from accumulated data. While it is well known that there is the possibility to re-estimate the number of patients needed for a study based on observed data in interim analysis, it is less known that design can allow objectives to be achieved in one study that would normally require the scheduling of two distinct studies, so called "seamless Phase II/III designs". 

Seamleass Phase II/III designs combine two sequential and separate studies into one study and allows the use of collected information in the first stage to adapt the design in the second stage. The advantages in this design are a reduction in overall time in the development of a drug, fewer patients required, and the early availability of long-term safety data.

Another interesting possibility of an adaptive design is to select the right target population for the drug. This is of special interest in the development of highly specific cancer drugs, which are only effective in a selected patient populations - for example, monoclonal antibodies. An adaptive study in Phase II/III may allow for the selection of the most promising target population in the first stage for the second stage. 

Based on CROS NT's experience in oncology trials, our expert biostatisticians recommend the following for late phase adaptive trials:
  • Budget more time for planning of an adaptive design as compared to a standard design
  • Interact with regulatory authorities in the planning phase especially for Phase II/III studies
  • Use simulations to calculate the power of the sample size and the probability of success
  • Evaluate whether or not to stop the recruitment of patients for the interim analyses
  • Schedule frequent monitoring visits in order to provide as much data as possible for the interim analyses
For training and examples of adaptive trial design for oncology research, join Thomas Zwingers' German-language webinar - "Statistical Considerations in Oncology Part II" (Statistiche Überlegungen für klinische Studien in der Onkologie Teil 2) which is taking place on Friday, 6th of June at 15:00. To register, click here.

If you are interested in statistical consultancy with Thomas Zwingers, please send us an inquiry. Thomas consults on trial design, methodology and regulatory submissions and has particular expertise in adaptive trial design and oncology research.