- 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
Friday, March 7, 2014
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:
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.
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.
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
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 firstname.lastname@example.org. 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.
Tuesday, February 11, 2014
CROS NT examines the implications of the challenges companies face in developing oncology therapies and how implementing good study designs can decrease the probability of failure in early phases.
We've already discussed what makes oncology a unique therapeutic area:
- Long timelines to reach clinical endpoints
- The use of treatment combinations
- The large number of partially related diseases
- The importance of disease sub-types and/or genotypes
- Regimen modifications during treatment
- The high impact of the disease on patient life
- The high costs of treatment
- Slow Recruitment
What are the implications of these unique traits?
- Study Design in early phases is extremely important: safety and efficacy, ethical considerations and long patient recruitment need to be take into account.
- Patient Recruitment and Retention is challenging: biostatisticians should be involved in the beginning to define protocol requirements.
- There will be vast amounts of data to analyze, including SAE safety data, and therefore biostatisticians 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 oncology studies, the drug is tested for safety of drug combinations are tested to recommend dosage for Phase II. The cohort design of a Phase I trial tests for drug safety as well as efficacy. Phase I considerations for oncology include protocol planning, analysis of EDC solutions for real time data collection and centralizing clinical data in order to have access to the same biostatistician throughout the study and one central database.
Good Practice Designs: Involving the Biostatistician
The Statistical Analysis Plan for oncology trials, especially for Phase I trials, is extremely important in terms of determining trial design, sample size, endpoints and determining inclusion/exclusion criteria. The biostatistician should be involved in the beginning of an oncology study to consult on:
- Protocol Development
- Trial Design
- Defining the Study Objectives and appropriate design
- Defining the statistical method
- Defining hypothesis and testing procedures
Good Practice Designs can speed up the planning phase; a team with previous oncology experience will enable a reduction of time from the study synopsis to first patient in the study because it defines adequate target criteria, interim analyses and specifies the most efficient statistical method for analysis.
Standardization means establishing uniform technical specifications, criteria, methods, processes or practices, adverse effects documentation, QoL questionnaires and follow up information. Using these specific forms, it is possible to improve quality and reduce project costs through a validated database structure, validated SDTM and ADaM formats or ePRO and EDC solutions
Establishing criteria, methods, processes and practices for presenting results can optimize the output. Good practice designs in early phase oncology studies can reduce the risk of project failure and advance development to later phases where statisticians can revert to adaptive trial design to optimize late phase study outcomes.
CROS NT excels in the statistical design and analysis of oncology trials from Phase I-IV including Adaptive Trial Designs. For more information on how to optimize the outcome of your oncology trials and consult our expert biostatisticians, please contact us.
Wednesday, January 29, 2014
The FDA has released a position statement on study data standards for regulatory submissions. According to recent legislation in the U.S., the FDA "will develop guidance for the industry on the use of CDISC data standards for the electronic submission of study data in applications". The statement goes on to say that the FDA will publish guidance that "requires study data in conformance to CDISC standards".
CROS NT discusses adopting and implementing CDISC standards as well as preparing a project mapping plan with your CRO.
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.
However, there are many legacy studies with valuable data that have not been conducted to this standard. This can create issues when trying to integrate data from different studies. To correct this problem it often requires considerable mapping work which can be expensive.
When requesting a CRO or an internal team to map a set of legacy studies to CDISC standards, requesting the following deliverables will ensure a structured and validated approach.
1. Project Plan and Project Charter to establish contacts, timelines, frequency of status reports, project risks and issue resolution procedure.
2. Standardized Data using SDTM format, including the source of the original data (specify version of SAS to be used)
3. Mapping documents between the source and target data, to provide traceability information
4. Data Status Table containing the different source data characteristics including quantitative and qualitative measurements
5. Metadata File (define.xml)
6. Mapping Program files and execution logs (.log) which documents the mapping process
7. Reviewers Guide which contains a detailed description of the mapping rules and source data types for each study
8. Validation Certificate containing signed reports of quality control checks including resumes of the personnel involved
Using CDISC standards can ensure much better traceability of data, especially in cases where there is one global data warehouse. The statistical programming team prepares ADaM datasets for both traceability and analysis-ready requirements. Analysis-ready datasets, according to CDISC "ADaM Implementation Guide Version 1.0", means minimal programming is required and no derivations should be done during programming of the statistical analysis but all variables and observations should be included in the dataset.
Implementing CDISC standards means adopting technologies that can manage the flow of data and information. Keeping clinical data in multiple repositories can create a complex management process and is often prone to error. Setting up a central database for all data - whether from a laboratory, a CRF or an ePRO device - to be stored in one place, allows for an easier mapping process.
CROS NT is a CDISC Gold Member, meaning it has constant access to new data standards and new documentation regarding CDISC standards. CROS NT has helped many companies incorporate CDASH, SDTM and ADaM standards into their organizations and mapped legacy studies to create the necessary consistency in formats. The programming team has developed some excellent macros to reduce time, costs and ensure consistency.
In a recent vaccine study, CROS NT applied CDISC standards for an integrated summaries submission to the FDA. This involved creating an annotated CRF, implementation of data structures within SAS, and creating a global database structure. Following initial consultancy to discover a Sponsor's needs, CROS NT can offer:
- Mapping of raw data to SDTM and ADaM standards
- CDASH libraries to provide competitive EDC usage fees
- Macros/tools to ensure fast and accurate mapping
- Data repositories with flexible data access and reporting capabilities
CROS NT has also provided consultancy on CDISC mapping in various studies including a Phase III oncology study and a Phase IV respiratory study.
Thursday, January 23, 2014
CROS Academy, the training arm of CROS NT, is pleased to announce its first webinar - "Adaptive Trial Design in Medical Device Trials - Determining When and How to Implement the Adaptive Approach".
CROS NT invites those who are responsible for conducting medical trials to join our free webcast on implementing adaptive trial design methodology in medical device trials. The webinar will take place on Thursday, 20th of February at 15.00 CET. The webcast is being conducted by two highly respected biostatisticians in the fields of adaptive trial design and medical device trials respectively.
Thomas Zwingers, expert in Adaptive Trial Designs, leads the webinar while George DeMuth, expert statistician for medical device trials and FDA submissions, is serving as moderator for the webcast.
Webinar Background and Overview:
The European Union recently approved legislation for a new approval system of high tech medical devices that mirrors the approval process of the FDA. Some of the proposed changes include eliminating the equivalence principle, focusing more on post-market studies and requiring more data transparency. These changes add to an environment where some medical device trials can already be very complex and expensive. Hence, companies should be prepared to explore all the possible options in order to ensure their studies are both cost-efficient in terms of design and meet all regulatory requirements.
Implementing an Adaptive Trial Design approach is one solution for improving the conduct of medical device trials. Since statisticians involved in medical trials must balance potentially expensive products, long follow-up periods, possibly large sample sizes, and unique endpoints, Adaptive Trial Design can help account for these changes.
However, it is important to note that Adaptive Trial Design is not always the appropriate statistical methodology. This webinar demonstrates when and if Adaptive Trial Design should be implemented and how to incorporate this methodology in your study design. The presentation also includes case study examples of successful application of Adaptive Design.
About Thomas Zwingers
Thomas Zwingers is the Senior Director for Consultancy Services and Senior Biostatistician for CROS NT. He has been working in the clinical trial environment for more than 30 years specializing in project management and statistical analysis and reporting. His expertise lies in Adaptive Trial Design, meta-analysis and interim analysis, cohort studies and DSMB support. Thomas has a Master's degree in Cybernetics from the Technical University of Munich, and has written and collaborated on over 100 publications during his career. He is based in CROS NT's Augsburg, Germany office.
Webinar Details and Information
Date: 20 February 2014
Time: 15.00 CET
Registration: To register for this webinar, please complete the online registration form. Webinar connection details will be send via email prior to the event.
For any additional questions, please contact us at email@example.com.
Wednesday, January 15, 2014
CROS Academy is pleased to announce it has scheduled its first biostatistics courses for 2014. CROS NT created CROS Academy in 2011 as the training arm of the company to provide professional courses and webinars from top statisticians within the CROS NT organization as well as external consultants.
In the first half of 2014, CROS NT is bringing back its famous "Understanding the Statistical Elements of a Study Protocol - for Non-Statisticians" course. The first course has been scheduled for Thursday, 30th of January in Milan, Italy and will be conducted in Italian by statistician, Paolo Fina. The second course is scheduled for Thursday, the 3rd of April in Munich, Germany and will be lead by Thomas Zwingers in German.
Course Description (in English, Italian and German) as follows:
Understanding the Statistical Elements of a Study Protocol - for Non-Statisticians
This course is offered to clinical trial professionals who wish to comprehend the role of statistics and statisticians in clinical trials. The course aims to enhance one's understanding of basic statistical principles as well as the statistician's view on clinical trial aspects. After this course, participants will be able to understand key concepts in clinical trial statistics, recognize and understand key terminology such as sample size calculation, confidence intervals and protocol and communicate more effectively on the role of statistics in clinical trials.
Comprendere gli Elementi Statistici di un Protocollo di Studio - per non-statistici
Questo corso è rivolto ai professionisti della Ricerca Clinica che desiderano comprendere al meglio il ruolo della statistica e degli statistici nell'ambito degli studi clinici. Il corso ha lo scopo di aumentare la conoscenza dei principi statistici di base. Dopo questo corso i partecipanti saranno in grado di comprendere i concetti chiave delle statistiche nella Ricerca Clinica, di riconoscere e di comprendere la terminologia utilizzata nei protocolli incluso il calcolo del campione e gli intervalli di confidenza e di comunicare in modo più efficace il ruolo che la statistica ricopre nella Ricerca Clinica.
Giovedì, 30 Gennaio 2014
Radisson Blu Hotel - Via Vallapizzone 24, Milano | 09:00 - 17:00
Einsicht die Statistischen Elemente eines Studienprotokolls für Nicht-Statistiker
Dieser Kurs wird Profis in klinischen Studien angeboten, die Interesse daran haben, den Stellenwert der Statistik und die Rolle des Statistikers in klinischen Studien zu verstehen. Der Kurs soll das Verständnis von grundlegenden statistischen Prinzipien verbessern, sowie den statistischen Blick auf klinische Studien schärfen. Nach diesem Kurs werden Sie in dar Lage sein: Die grundlegenden Konzepte der Statistik in klinischen Studien zu verstehen, wichtige statistische Fachbegriffe wie Fallzahlberechnung, Konfidenzintervalle und Studiendesign zu verstehen und effektiver kommunizieren zu können, welche Rolle die Statistik in klinischen Studien spielt.
Donnerstag, 03. April 2014
Eden Hotel Wolff - Arnulfstrasse 4, Maxvorstadt, München
CROS Academy is planning 5 additional courses throughout 2014 including its Introduction to Adaptive Trial Design course and a new course titled "Understanding Study Design and Sample Size Estimation". For updates on CROS Academy courses, be sure to check the website.
CROS Academy is also adding webinars to its course listings this year. We will have information on our first webinar in February soon.
Additionally, CROS Academy can offer on-site training for companies who are interested in company-wide biostatistics training for its employees. If you are interested full day trainings or lunch seminars in biostatistics are your company offices, please send an email to firstname.lastname@example.org.