THE ONLY THREE (3) [PROGRAMMING] LANGUAGES YOU SHOULD LEARN RIGHT NOW (ECLINICAL SPEAKING)
- Details
- Written by Super User
- Category: Clinical Data Management
On a previous article that I wrote in 2012, I mentioned 4 programming languages that you should be learning when it comes to the development of clinical trials.
Why is this important, you may ask? Clinical Trials is a method to determine if a new drug or treatment will work on disease or will it be beneficial to patients.
If you have never written a line of code in your life, you are in the right place. If you have some programming experience, but interesting in learning clinical programming, this information can be helpful.
But shouldn't I be Learning ________?
Here are the latest eClinical programming languages you should learn:
1. SAS®:Data analysis and result reporting are two major tasks to SAS®programers. Currently, SAS is offering certifications as a Clinical Trials Programmer. Some of the skills you should learned are:
- clinical trials process
- accessing, managing, and transforming clinical trials data
- statistical procedures and macro programming
- reporting clinical trials results
- validating clinical trial data reporting
2. ODM/XML: Operational Data Modeling or ODM uses XML to build the standard data exchange models that are being developed to support the data acquisition, exchange and archiving of operational data.
3.CDISC Language: Yes. This is not just any code. This is the standard language on clinical trials and you should be learning it right now. The future is here now. The EDC code as we know it will eventually go away as more and more vendors try to adapt their systems and technologies to meet rules and regulations.
Some of the skills you should learn:
- Annotation of variables and variable values - SDTM aCRF
- Define XML - CDISC SDTM datasets
- ADaM datasets - CDISC ADaM datasets
CDISC has established data standards to speed-up data review and FDA is now suggesting that soon this will become the norm. Pharmaceuticals, bio-technologies companies and many sponsors within clinical research are now better equipped to improve CDISC implementation.
Everyone should learn to code
Therefore, SAS® and XML are now cooperating. XML Engine in SAS® v9.0 is built up so one can import a wide variety of XML documentation. SAS® does what is does best - statistics, and XML does what it does best -creating report quality tables by taking advantage of the full feature set of the publishing software. This conversation can produce report-quality tables in an automated hands-off/light out process.
Standards are more than just CDISC
If you are looking for your next career in Clinical Data Management, then SAS and CDISC SDTM should land you into the right path of career development and job security.
Conclusion:
Learn the basics and advanced SAS clinical programming concepts such as reading and manipulating clinical data. Using the clinical features and basic SAS programming concepts of clinical trials, you will be able to import ADAM, CDISC or other standards for domain structure and contents into the metadata, build clinical domain target table metadata from those standards, create jobs to load clinical domains, validate the structure and content of the clinical domains based on the standards, and to generate CDISC standard define.xml files that describes the domain tables for clinical submissions.
Need SAS programmers? We can help provide resources in-house / off-shore to facilitate FDA review by supporting CDISC mapping, SDTM validation tool, data conversion and CDASH compliant eCRFs.
- Hits: 3476
List of Validation Rules for SDTM Compliance
- Details
- Written by Super User
- Category: eClinical
In 2014, the FDA published its first official list of validation rules for CDISC SDTM study data standards. These rules cover both conformance and quality requirements, as described in the FDA Study Data Technical Conformance Guide. Conformance validation rules help ensure that the data conform to the standards, while quality checks help ensure the data support meaningful analysis.
With the help of the implementation guide, which provides documentation of metadata (data about the data) for the domain datasets that includes filename, variable names, type of variables and its labels, etc.; we can then map to the CDISC Analysis Data Model (ADaM) to be able to submit to the regulatory agency. This provides a clear content, source and quality of the datasets submitted in support of the statistical analysis performed by the sponsor.
Learn the basics on how to implement CDISC data standards concepts on your clinical trials from study design to FDA data analysis submission.
Need SAS programmers, CDISC Subject Matter Experts (SMEs) or a clinical programmer? We can help provide resources in-house / off-shore to facilitate FDA review by supporting CDISC mapping, SDTM validation tool, data conversion and CDASH compliant eCRFs.
- Hits: 3124
SOLVING DATA COLLECTION CHALLENGES
- Details
- Written by Super User
- Category: EDC
Cross-partnership between sponsors and CROs for the collection and analysis of clinical trial data are complex. As a result there are a number of issues encountered during the running of trial.
As with many projects, standardization projects like CDISC is a huge undertake. It requires resources, technology and knowledge-transfer. The industry (FDA for example) has been working on standardization for years but on September 2013, it became official, in which the FDA released a 'Position Statement'.
Data Collection
According to the WHO, data collection is defined as the ongoing systematic collection, analysis, and interpretation of health data necessary for designing, implementing, and evaluating public health prevention programs.
Sources of data: primarily case report books or (e)CRF forms, laboratory data and patient report data or diaries.
Challenges of data collection
It is important for the CROs / service providers to be aware of the potential challenges they may face when using different data collection methods for partnership clinical studies. Having several clients does not mean having several standards or naming conventions. This is the main reason why CDISC is here. So why are many CROs or service providers not using CDISC standards?
Another challenge is time limitations. Some clinical trials run for just a few weeks / months.
It may be found difficult to understand the partnership in the amount of time they have. Hence, most CROs and service providers prefer to perform manual mapping at the end of the trial, hence, re-work and manual work.
Funding also plays a key challenge for CDISC-compliance data collection study. Small researchers or biotechnology companies that do not have the resources in-house, out-sourced this task to CROs or service providers and are not interested whether it is compliance as long as it is save them money. But would it save money now instead of later in the close-out phase?
If there is a shortage of funding this may not allow the CRO or service provider all the opportunities that would assist them in capturing the information they need as per CDISC standards.
We really don't have the level of expertise or the person dedicated to this that would bring, you know, the whole thing to fruition on the scale in which it's envisioned - Researcher
Role of the Library
There is a clear need for libraries (GL) to move beyond passively providing technology to embrace the changes within the industry. The librarian functions as one of the most important of medical educators. This role is frequently unrecognized, and for that reason, too little attention is given to this role. There has been too little attention paid to the research role that should be played by the librarian. With the development of new methods of information storage and dissemination, it is imperative that the persons primarily responsible for this function should be actively engaged in research. We have little information at the present time as to the relative effectiveness of these various media. We need research in this area. Librarians should assume an active role in incorporating into their area of responsibility the various types of storage media. [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC232677/]
Review and Revise
At the review and revise stage it might be useful for the CRO or service provider to consider what the main issues are when collecting and organizing the data on the study. Some of these issues include: ensuring sponsors, partners and key stakeholders were engaged in the scoping phase and defining its purpose; the objectives have been considered; the appropriate data collection methods have been used; the data has been verified through the use of multiple sources and that sponsors have approved the data that is used in the final clinical data report.
Current data management systems must be fundamentally improved so that they can meet the capacity demand for secure storage and transmission of research data. And while there can be no definitive tools and guideline, it is certain that we must start using CDISC-standards from the data collection step to avoid re-inventing the wheel each time a new sponsor or clinical researcher ask you to run their clinical trial.
RA eClinica is a established consultancy company for all essential aspects of statistics, clinical data management and EDC solutions. Our services are targeted to clients in the pharmaceutical and biotech sector, health insurers and medical devices.
The company is headquarter in Panama City and representation offices with business partnersin the United States, India and the European Union. For discussion about our services and how you can benefit from our SMEs and cost-effective implementation CDISC SDTM clinical data click here.
- Hits: 3851
Electronic Data Capture (EDC) Future
- Details
- Written by Anayansi Van Der Berg
- Category: EDC
While EDC has been around for many years, we all acknowledge that the percentage of EDC studies has just moved into the majority. Some of the reasons for such a slow uptake include:
1) failure to understand that EDC adoption requires significant change to fundamental business processes hindering pharma from leaving "paper" processes behind;
2) thinking that studies "outsourced" to an EDC vendor means minimal change to a clinical trials organization; and
3) continued evolution of EDC tools and vendors leading to some pharma companies chasing what appears to be the newest and best solution.
One key issue arising from EDC adoption is its disruptive effect on clinical trial site staff. Now, with the majority of clinical trials using web-based EDC tools, clinical site staff are reaching a point where they say no more EDC because the volume of EDC studies severely impacts their ability to maintain source documents and use multiple EDC tools. For example, for one EDC tool, clinical site staff must take training from each sponsor using the tool which produces a huge time burden on those individuals. Broad adoption of EDC (eCRF) standards from efforts like CDISC's CDASH initiative may alleviate some of that disruption, but clinical site process considerations are largely being ignored. Remember, those folks are critical customers for EDC.
So, there are multiple factors creating problems for EDC adoption and their common threads all point to understanding business processes inside pharma and inside clinical trial sites. Addressing those factors should help EDC move from a simple majority to larger adoption.
The huge number of purely EDC vendors presents problems for the buyer (pharma companies). Many companies are now looking to consolidate numerous vendors to a select few. Therefore, many are looking further into the future than simply choosing an EDC vendor. They may also need an IVRS vendor or perhaps help with the trial design and data monitoring. Many of the EDC vendors cannot provide this one stop shopping. There is a legitimate concern on the part of companies that an EDC vendor may not be around in a couple of years. Many have fallen by the wayside in recent years purely because they could not compete or did not address the needs of the client.
Furthermore, there is a desire for consistency. Difference clinical groups within a company can have different preferred vendors. This can compound the problem of data collection and integration. Lastly, there is the management of the collected data--who owns it, who has access, etc. Internal politics can play a big role here. Many companies (and clinical programs) may simply decide that building is better than buying.
The main problem with EDC in the pharma industry is one of business process change. For many, the technology is very sound, and replaces labor-intensive and cost-intensive historic paper-based approaches. However, the burden of labor shifts from internal data entry staff to site-specific clinical/medical staff. There is an immediate improvement in reducing the number of queries, and that improvement, along with the speed with which data is available, is where many of the benefits reside. THe cost of moving this work to the site is non-trivial.
Research companies can gain more success with EDC systems by altering the business process within their organizations. Because the data is more timely, and more accurate earlier in the process, what else can be done within the business process to leverage the investment in EDC?
EDC is not just about moving data faster, it's about leveraging the EDC investment of optimize the process whereby new therapies are ultimately approved for use.
RA eClinica is a established consultancy company for all essential aspects of statistics, clinical data management and EDC solutions. Our services are targeted to clients in the pharmaceutical and biotech sector, health insurers and medical devices.
Consultants available for study setup, including CRF design, edit checks and CDISC SDTM clinical data click here.
- Hits: 3488
CDISC/CDASH Standards at your Fingertips
- Details
- Written by Super User
- Category: Industry Tips
A standard database structure using CDISC (Clinical Data Interchange Standards Consortium) and CDASH (Clinical Data Acquisition Standards Harmonization) standards can facilitate the collection, exchange, reporting, and submission of clinical data to the FDA and EMEA. CDISC and CDASH standards provide reusability and scalability to EDC (electronic data capture) trials.
There are some defiance in implementing CDISC in EDC CDMS:
1. Key personnel in companies must be committed to implementing the CDISC/CDASH standards.
2. There is an initial cost for deployment of new technology: SDTM Data Translation Software, Data Storage and Hosting, Data Distribution and Reporting Software.
3. It can be difficult to understand and interpret complex SDTM Metadata concepts and the different implementation guides.
4. Deciding at what point in a study to apply the standards can be challenging: in the study design process, during data collection within the CDMS [CDASH via EDC tools], in SAS prior to report generation [ADaM], or after study completion prior to submission [SDTM].
5. Data management staff [CDM, clinical programmers], biostatisticians, and clinical monitors may find it difficult to converge on a new standard when designing standard libraries and processes.
6. Implementing new standards involves reorganizing the operations of (an organization) so as to improve efficiency [processes and SOPs].
7. Members of Data Management team must be retrained on the use of new software and CDISC/CDASH standards.
8. There are technical obstacles related to implementation in several EDC systems, including 8 character limitations [SAS] on numerous variables, determining when to use supplemental qualifiers versus creating new domains, and creating vertical data structure.
Anayansi Van Der Berg is a gold member of the CDISC organization providing support to CFAST projects. She is available for consulting projects.
- Hits: 2860