Tuesday, April 15, 2014

Webinar: Statistische Überlegungen für klinische Studien in der Onkologie

CROS NT lädt Sie im Mai zu unserem kostenlosen zweiteiligen Webinar über Statistische Überlegungen in der Onkologie ein. Dieses Webinar wird von dem renommierten deutschen Biostatistiker Thomas Zwingers geleitet, der über besondere Fachkenntnisse in onkologischen Studien verfügt.

Webcast Einzelheiten

Datum:
Teil 1: Donnerstag, 8. Mai 2014
Teil 2: Donnerstag, 29. Mai 2014

Zeit:
15:00 CET

Dauer:
Vorlesung: 1 Stunde
Q&A: 30 Minuten

Webinar Übersicht:

Onkologie ist ein Hauptschwerpunkt für viele pharmazeutische und biotechnologische Unternehmen auf der ganzen Welt. Krebs kostet mehr als 7 Millionen Menschen im Jahr das Leben und hat über 450 verschiedene IndikationenDas zweiteilige Webinar beschreibt warum Onkologie ein einziger Bereich ist und die statistischen Methoden am besten für diese Studien geeignet sind.

Teil 1: Voraussetzungen für den Erfolg von Studien in den Entwicklungs-Phasen I und II
Die Phase I und Phase II der Produktentwicklung legen den Grundstock für einen Erfolg Phase III. Speziell in der Onkologie hat die Heterogenität der Erkrankungen einen imensen Einfluss auf die Planung von klinischen Studien.

Im Seminar werden die wichtigsten Einflussfaktoren erläutert und ihr Einfluss auf die konkrete Planung dargestellt. Dabei wird insbesondere auf die Problematik der Zielkriterien und Surrogat-Marker eingegangen.

Neue Studiendesigns, die es ermöglichen, die Patientenzahlen in den frühen Phasen zu minimieren, werden vorgestellt.

Teil 2: Statistische Methoden  für frühzeitige Entscheidungen in den Phasen II und III
In der Phase III der Entwicklung  soll konfirmatorisch die Wirksamkeit des Produktes belegt werden. Viele neue Produkte haben einen sehr speziellen Wirkungsmechanismus und daher ist es oft notwendig Subpopulationen zu finden, die entweder besonders gut oder besonders schlecht auf die neue Behandlung anspechen.

Flexible Studiendesigns, die es erlauben, möglichst frühzeitig und unter Verwendung von möglichst wenigen Patienten eine erfolgreiche Basis für die Feststellung der klinischen Wirksamkeit werden mit ihren Vor- und Nachteilen vorgestellt.

Das Webinar ist kostenlos und wird von der CROS Academy organisiert.

Der Kursleiter des Webinars
Thomas Zwingers ist der Senior Direktor of Consultancy Services und Senior Biostatistiker für CROS NT. Seit über 30 Jahren arbeitet er im Umfeld klinischer Studien und ist spezialisiert in Projektmanagement, statistische Analysen und Berichterstellung. Sein Fachwissen liegt im adaptiven Studiendesign, Meta-Analysen und Zwischen Auswertungen, Kohortenstudien und DSMB Unterstützung. Thomas hat einen Masterabschluss in Kybernetik von der Technischen Universität München und hat im Laufe seiner Karriere mehr als hundert Veröffentlichungen als Autor und Co-Autor publiziert. Er sitzt in der deutschen Niederlassung von CROS NT in Augsburg.



Thursday, April 10, 2014

Good Luck to our CROS NT London Marathon Runners!

CROS NT is proud to announce that two of its employees will be running the Virgin Money London Marathon this Sunday, 13th of April. The London Marathon is one of the world's largest and well-known marathon races, and the event raises significant funds for various charitable causes.

CROS NT's Paul Terrill and his costume for the London Marathon
Representing CROS NT will be CEO, Andrew MacGarvey who is running and raising money for the Children with Cancer organization. Also representing CROS NT will be Paul Terrill, Principal Biostatistician in the CROS UK branch, who is running for Great Ormond Street Childrens' Hospital. 

CROS NT has been active in the past year supporting various charities through running events including 5K runs at last year's DIA Annual Meeting to support One Fund Boston. 

Andrew MacGarvey has also set up the Runners in Life Sciences group on LinkedIn for avid runners in the life sciences sector to connect and organize runs at various conferences.

Best of luck to our runners in this year's London Marathon!


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Sunday, April 6, 2014

New legislation passed on clinical data transparency- is your company prepared?

As of April 2014, the European Union passed legislation which will require clinical trial Sponsors to publish detailed summaries of clinical trial data in a database accessible to the public upon marketing authorization. CROS NT provides recommendations on how to prepare clinical data for greater transparency for the public and greater traceability for regulatory authorities.

The European Medicine Agency's initiative on data transparency was finally passed into draft law by the European Union, meaning that clinical trial data will no longer be confidential once marketing authorization is granted and Sponsors could face strict fines for not complying. The legislation is anticipated to take effect in 2016 with the support of the EMA.

The EU states its objectives in the opening section of the legislation, "In a clinical trial the rights, safety, dignity and well-being of subjects should be protected and data generated should be reliable and robust." 

Additionally, the public database will contain full clinical trial summaries - including clinical study reports.

These latest developments signal a significant step towards greater clinical trial data transparency in the European Union, and clinical trial Sponsors need to evaluate whether they are prepared for the upcoming legislation.

Centralizing biometrics means all your data is stored in a central warehouse and/or archive which avoids having to keep track of multiple repositories. Centralizing clinical data in the early phases of drug development facilitates better integration of studies across all phases with common assessment methods, uniform traceability of data as well as the centralization of study metrics and reports.

Clinical data visualization can be important for Sponsors conducting trials in Europe who need to make informed decisions and make sense of clinical data which could eventually be shared publicly. Clinical leaders can see information that is beyond the capability of the CTMS report set. In addition, it facilitates Risk Based Monitoring which vastly improves data quality and cuts monitoring costs.

As a CRO specialized in biometrics, CROS NT can help companies make regulatory submissions to the EU and USA prepare their clinical data in a reliable and traceable way, and help remove patient identifiers if necessary. Our expert biostatisticians and clinical data management teams have completed over 1,000 studies and we have provided statistical consultancy for pre- and post-marketing studies including support at meetings with regulators. We are a CDISC Gold Member and we also offer integrated EDC, ePRO, IWRS, CTMS and reporting.

Thursday, March 20, 2014

Preparing for Data Transparency: EMA publishes first summary of a Risk Management Plan

This month, the European Medicines Agency (EMA) published the first summary of a risk management plan (RMP) for a newly authorized medicine. An RMP is a publicly available document that describes all that is known and unknown about a drug's safety and what actions will be taken to monitor the drug on the market and mitigate any risks. CROS NT explores what this means for your clinical data.

With this news, the EMA published a statement saying, "the Agency will pilot the publishing of RMP summaries for all newly centrally authorized medicines during 2014 and at a later stage will start producing RMP summaries for previously authorized medicines".

The publication of RMPs is a step towards greater clinical transparency in the European Union. RMPs include:
  • A medicine's safety profile
  • Plan to prevent or minimize risks for patients
  • Plans for studies and other activities to gain more knowledge about the safety and efficacy of the medicine
  • Risk factors for developing side effects
  • Measuring the effectiveness of risk-minimization measures 
How can companies prepare their clinical data for greater transparency?
The European Union RMP includes three components: (1) a summary of the drug's safety based on previous pre-clinical and clinical studies, (2) the pharmacovigilance plan, and most importantly, (3) the risk minimization plan which provides preventative actions from doctors, pharmacists and clinical trial professionals, including statisticians.

Centralize Clinical Data from the Start
If one study is assigned to statistical trial design, data management, data analysis and medical communications from the start, common data standards can be applied throughout the drug development process. Continuity of team members creates a consistent style of medical communications and important collaboration between statistician, data manager and medical writer. All data are stored in a central data warehouse and/or archive which avoids having to keep track of multiple repositories. 

Centralizing clinical data in the early phases of drug development facilitates better integration of studies across all phases with common assessment methods, uniform traceability of data as well as the centralization of study metrics and study reports. 

Ensuring Traceability for Regulatory Submissions
In order for data to be transparent to the public, it must also be traceable. Implementing CDISC standards helps both traceability and cross analysis of datasets. There must be clear traceability from analysis results, to analysis datasets, and to SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variables or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan. Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM datasets together with algorithms and methods used to derive an analysis result.

Invest in Clinical Data Visualization Tools
Conducting a trial generally leads to data being spread across multiple databases, including EDC, CTMS, ePRO, safety databases etc, and if a centralized approach was not employed, such databases can be spread across multiple vendors. Data visualization tools allow the ability to drill-down data and click-through multiple levels of detail, allowing for the analysis of specific subsets and sub-populations. Customizable dashboards allow the clinical team to create ad hoc reports on site performance, data quality, safety and efficacy, drug supply and patient management.

However, perhaps the biggest benefit of data visualization, is that clinical metrics from multiple sources can be analyzed in real time. Moreover, decision-makers can identify and fix underperforming sites and make crucial decisions on study progress.

As a CRO specialized in biometrics, CROS NT can help companies making regulatory submissions in the EU and USA prepare their clinical data in a reliable and traceable way. Our expert biostatisticians and clinical data management teams have completed over 800 studies and we have provided statistical consultancy for pre- and post-market studies including support at meetings with regulators. We are a CDISC Gold Member and we also offer integrated EDC, ePRO, IWRS, CTMS and reporting.


Friday, March 7, 2014

CROS Academy Announces New Course on Study Design and Sample Size Estimation

CROS Academy has introduced a new course into its 2014 lineup - "Understanding Study Design and Successful Samples Size Estimation" - to expand its training selection in the critical area of biostatistics. This course was created after the success of our "Understanding the Statistical Elements of a Study Protocol" course which is aimed at teaching biostatistics principles to non-statisticians with the goal of increasing understanding across the broader spectrum of clinical development. 

"Understanding Study Design and Successful Sample Size Estimation" takes place on Tuesday, 27th of May in London, United Kingdom.

This course divulges a bit further into the importance of statistical methodology for successful study design and how sample size calculation can significantly impact the success or failure of a trial. 

Intelligent study design with appropriate statistical methods can separate projects that ought to fail from those that deserve to succeed. Choosing the wrong endpoints or wrong statistical analysis can increase costs considerably and have an effect on the entire study team. 

Understanding the biostatistician's role in study design is the first step towards successful collaboration in the trial. This includes understanding the various study designs for various drug development phases and having a fundamental understanding of descriptive statistics like endpoints and parametric vs. non-parametric methods.

The second part of this course focuses on successfully calculating and interpreting sample size. Under estimation can result in the lack of statistical significance in clinical trial results. On the other hand, over estimation can lead to ethical and safety issues if too many patients are exposed to a test drug.

This course is offered to clinical trial professionals who wish to better comprehend the critical success factors of study designs and sample size estimation from a statistical viewpoint. At the end of this course, participants should be able to:
  • Understand key concepts in clinical trial statistics
  • Understand how statistics are reflected in the design of clinical trials
  • Understand the concept of Sample Size Calculation: how is it calculated and how to interpret the results
  • Communicate more effectively on the role statistics play in smart drug development design
For more information or to register for this course, download the brochure from our website.

Course Instructor - Thomas Zwingers
The instructor for this course will be CROS NT's Senior Biostatistician, Thomas Zwingers who leads CROS Academy's biostatistics training program. Thomas provides pharma, biotech and medical device companies with statistical methodology advice pertaining to trial design, conduct and reporting including regulatory submission. He has been working in the clinical trial environment for over 30 years and specializes in Adaptive Trial Design and Bayesian Framework, Meta-Analysis and Non-Inferiority Trials.

Monday, February 24, 2014

Centralized Biometrics and Cloud-Based Technology for Clinical Trials: A Win-Win Approach to Operational and Cost Efficiency

The eClinical trial technologies market is set to reach $1.37 billion USD by 2018. The driving forces behind this surge is the need to optimize the drug development process through real-time data analysis while cutting costs along the way. While various sectors such as small and large pharmaceutical companies, biotech companies and medical device companies have different trial needs, a strategic outsourcing approach that optimizes data collection and analysis can benefit any company in these sectors. 

In anticipation of U.S. ePharma Day - East Coast Edition, which highlights how to implement technology into clinical trial strategy, CROS NT and DATATRAK discuss how the benefits of a centralized biometrics outsourcing strategy with cloud-based technology can significantly improve the efficiency and accuracy of your next clinical study.

Centralized Biometrics
Using a centralized biometrics provider ensures a high level of importance and attention is given to clinical trial data. While it may be obvious how large pharma can benefit from centralizing data with volume discounts and improved efficiency through standard data formats, the same model can be applied to small pharma, biotech and medical device companies.

Efficiencies include standardized data formats, uniform traceability of data, due diligence ready datasets and documentation and a single project manager for all data services.

Involving the same biostatistician is also beneficial in order to provide expert consultancy on trial design and methodology, and/or apply methods to combine trial phases or adjust the sample size based on interim study results (adaptive trial design).

This model provides financial benefits for data management and statistical analysis activities as well as consistent use of technology solutions.

Cost Saving Estimation:
  • Clinical Data Management: With EDC, companies can save up to 40% in database set-up costs on 2nd and subsequent studies
  • Statistics: Companies can save up to 40% on statistical programming costs for follow-on studies
  • Overall estimate of savings through data centralization is 20-30%: For example, based on 3 studies per year over 3 years where the biometrics portion costs on average $200,000 per study, the cost could cost around $1,800,000. By centralizing the data, cost savings can be between $400-600K, and quality and turnaround time will improve.
Cloud-Based Technologies
Technology services, including EDC and ePRO solutions, pharmacovigilance applications, centralized storage and data visualization, can become part of the centralized biometrics bundled package. Using the same ePRO/EDC/IWRS reduces set-up, training and HelpDesk costs and allows for easy data integration. Only one set up fee for safety databases and volume discounts can be applied. 

With cloud-based eClinical applications, key performance indicators and the creation of clear Service Level Agreement with CROs becomes more achievable:
  • Sponsors are able to negotiate enforceable expectations for clinical data entry into the eClinical application
  • Pay based on data quality
  • Move away from time based unit of measurements to activity based measurements
  • Standard & ad-hoc reports
  • Industry and regulatory compliant archival records
An eClinical, cloud-based solutions allows for real-time study management, access to data from any device and flexibility of contracted resources.

CROS NT and DATATRAK are sponsoring and presenting at the inaugural U.S. ePharma Day - East Coast on Tuesday, March 25th 2014 in Cambridge, Massachusetts. This event focuses on trial strategy and the implementation of technology. It highlights the difficulties faced by those in charge of procuring technology in a sector where integrated systems are important, but still misunderstood. 

CROS NT joined the DATATRAK Connect Partner Program in 2013 to provide clinical trial technologies such as EDC, CTMS, IRT and ePRO in order to reduce study costs and improve the drug and device development process. 




Friday, February 21, 2014

Measuring Uncertainty in Medical Device Trials with Adaptive Designs

This week, CROS NT's expert biostatisticians, Thomas Zwingers and George DeMuth led a webcast on adaptive trial design in medical device trials. The idea behind this webinar was the convey the importance of biostatistics in measuring uncertainty in medical device trials and deciding when to implement an adaptive trial design approach, and more specifically, what needs to be adapted. The discussion on adaptive trial design methodology is important for knowing what regulatory authorities except for the submission of new devices. 

Biostatistics is the study of uncertainty and determining how to measure it and how to react based on the results. Medical Device trials face the following uncertainties:
  • Safety Problems
  • Unexpected treatment effects/safety issues
  • High variance 
  • Effects in secondary endpoints/subpopulation
Reducing uncertainty in the planning phases of device development can eventually reduce timelines, and inevitably, costs. 



Adaptive Trial Design is one way to achieve this.

What makes device development uncertainty different from drug development? While both test for proof of safety and efficacy, statistical methods in device studies test for estimation of effects while drug studies focus on hypothesis testing. 

An adaptive design clinical study is defined as a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypothesis based on analysis of data (usually interim data) from study subjects (according FDA guidelines).

Adaptive Designs modify aspects of the study without undermining the validity and integrity of the trial. 

Why do we adapt medical device trials?
  • We can't always rely on assumptions in the planning phase
  • To make mid-course corrections for trials
  • To include fewer patients in a trial less likely to succeed
  • To reduce the development phase timelines
  • To increase the chance that patients will receive an effective treatment
What do we adapt in medical device trials?
  • Early termination
  • Sample size re-assessment
  • Treatment allocation ratios
  • Treatment arms (dropping, adding arms)
  • Hypotheses (non-inferiority vs superiority)
  • Test statistics
  • Population (inclusion/exclusion criteria)
  • Combine trials/treatment phases (seamless designs)

FDA vs EMA Guidelines for Measuring Uncertainty
The FDA lays out guidelines for situations which raise concerns and should be avoided during the planning phase including: multiplicity and controlling Type I error, choice of analysis made after unblinded data of interim are available, changes in the primary endpoint and operational bias. 

The FDA also highlights guidelines for adaptive design study protocols:
  • Description of relevant information on study drug
  • Description of all possible adaptations
  • Summary of each adaptation with respect to statistical issues
  • Computer simulations
  • Details of analytic derivations
  • Details on blinding
The EMEA has considerations and requirements for design modifications such as minimal requirements for Type I error control, estimation of treatment effects with CI, ensuring that different stages can be combined and involvement with the Sponsor. With regards to design modification, the EMEA recommends keeping sample size reassessment blinded whenever possible and to avoid modification of the primary endpoint. 

In addition, the EMEA recommends:
  • Imbalanced randomization over stopping arms 
  • Randomization Ratio
  • Switching between non-inferiority and superiority trials should be hierarchical 
Lastly, what are the implications of adaptive design on device trial project management? In terms of statistical planning, the statistician will be needed for a longer period of time and should be regularly involved in the communication with trial personnel. A very detailed description of methods and adaptations have to be in the protocol along with setting up an independent Data Monitoring Committee. In order to have fast access to data and efficient data management, consider EDC solutions, a central database, central randomization, prompt data entry and efficient data cleaning processes.

For more information on the technical aspects of adaptive trial design or to consult with one of our biostatisticians directly for an upcoming study, please contact us at info@crosnt.com. Our expert statisticians have helped numerous companies implement adaptive trial design for both device and drug studies. Keep checking the CROS Academy website for upcoming courses and webinars in adaptive trial design.