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Category Archives: SMEs

FREELANCER / CONSULTANT / EDC DEVELOPER / CLINICAL PROGRAMMER

  • Setting up a project in EDC (Oracle InForm, Medidata Rave, OpenClinica, OCRDC)
  • Creation of electronic case report forms (eCRFs)
  • Validation of programs, edit checks
  • Write validation test scripts
  • Execute validation test scripts
  • Write custom functions
  • Implement study build best practices
  • Knowledge of the process of clinical trials and the CDISC data structure

 

 

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Technology Consultants

Technology Consultants

Our consultants are recognized for their experience, capabilities and knowledge. RA eClinical Solutions dedicates a Professional Project Team to your project to assure its success. Our team provides expertise in eClinical and eCDMS study design and implementation.

Our primary goal is to work in collaboration with our clients to provide systems and services that meet their business needs, while ensuring complete compliance with all applicable U.S. and International regulations and guidelines.

RA eClinical Technology Consultants

We provide expert-level services in the following areas:

  • Data Management including CRF Design and Protocol Review
  • Data Cleaning activities including report creation and SAS Listings (SAS®, Cognos and IReview/Patient Profiles)
  • Database Design and Development (OC RDC, OpenClinica, Rave, Medrio and InForm)
  • The Electronic Clinical Study Project Management Life Cycle (ePMC™) provided by our eClinical Team is a validated process covering every aspects of the project management life cycle. The process manages the various stages of the study, including: Project Initiation, Planning, Execution, Monitor / Control, and Closeout.

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 partners in 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.

 
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Posted by on December 8, 2015 in eClinical, EDC, Freelancers, Outsourcing, SMEs

 

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RAeClinica – Software Development

RAeClinica – Software Development

eClinical Trial Technology, EDC, CTMS, and Technology Integration-Software Development – Web Development and Clinical Research Organization – Contract Research Organization

Source: RAeClinica – Software Development

 
 

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Solving Data Collection Challenges

Solving Data Collection Challenges

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 partners in 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.

 

 

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The Only Three (3) [Programming] Languages You Should Learn Right Now (eClinical Speaking)

The Only Three (3) [Programming] Languages You Should Learn Right Now (eClinical Speaking)

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 reportquality 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.

 

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From Non-SAS Programmer to SAS Programmer Part II

From Non-SAS Programmer to SAS Programmer Part II

Previously, we wrote about how you can become a SAS Programmer with little or no programming background.

Today, I want to share a new link where you can download SAS Studio for free and practice. I have to give a thank to Andrew from statskom for the tip. Visit his blog for more SAS tips.

Here is a quick step on what you need in order to use the SAS University version for free provided by SAS:

1- Create a SAS profile and select the environment based on your operating system in order to download the SAS® University Edition. I  chose Oracle VirtualBox. The options available are: Oracle VirtualBox in Windows, Macintosh, and Linux operating environments.

2- You will receive an email where you can you download your SAS edition as per your selected environment on step 1. Click the link. It could take up to an hour for the entire program to download.

SAS University Edition

3-Go to https://www.virtualbox.org/wiki/Downloads to install the OracleVirtualBox.

4-Add the SAS University Edition vApp downloaded on step 2 to VirtualBox step 3.

OracleVM

5-Create a folder for your data and results.

6- Start the SAS University Edition vApp

7-Open the SAS University Edition by opening your web browser and typing  http://localhost:10080. From the the SAS University Edition: Information Center, click Start SAS Studio.

There you have it! You have now access to SAS and can start practicing your new programming language.

anayansigamboa sas studio anayansigamboa sas studio anayansigamboa sas studio anayansigamboa sas studio

For more information about the SAS University Edition, see the FAQs and videos at http://support.sas.com/software/products/university-edition/index.html.

For Data Management and EDC training, please contact RA eClinical Solutions.

 

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From Non-SAS Programmer to SAS Programmer

SAS Programmers come from many different educational backgrounds. Many has started their careers as a Data Manager in a CRO environment and grew to become a SAS programmer. Others have gone to college and pursued degrees in math, statistics or computer science degree.

Do you have SAS Skills? First, you need to find out more about statistical programming desire skills and start to slowly learn what SAS programmers and statisticians do in the pharmaceutical industry. It is also important to understand the Drug Development and Regulatory process so that you have a better understanding of the industry as a whole as well as the drug approval process.

In addition, I have personally attended several workshop on Statistics for Non-statistician provided by several of my past employers/clients (GSK, Sanofi-Aventis, etc) so I could have a greater understanding of statistics role. I am personally more inclined to the EDC development than becoming a biostatistician but these are just some of the few steps you could take to grow your career as a SAS programmer.

Practice, Practice, Practice!

To begin learning how to actually program in SAS, it would be a good idea to enroll to a SAS course provided by the SAS Institute near you or via eLearning. I have taken the course SAS Programming 1: Essentials, and I would recommended. You could also join SUGI conferences and other user groups near your city/country. Seek every opportunity to help you gain further understanding on how to efficiently program in the pharmaceutical industry. It could well land you a Junior SAS programming position.

Transitioning to a SAS Programming role: Now that you have gotten your first SAS programming job, you will need to continue your professional development and attend additional training, workshops, seminars and study workgroup meetings. The SAS Institute provide a second level, more advance course Programming II: Manipulating Data with the Data Step, SAS Macro Language and SAS macro Programming Advanced topics. There are also SAS certifications courses available to help you prepare to become a SAS certified programmer.

There is a light at the end of the tunnel: Advance!

Your ongoing development will be very exciting and challenging. Continued attending SAS classes as needed and attending industry related conferences such as PharmaSUG to gain additional knowledge and insight on how to perform your job more effectively and efficiently.

As you can see, it is possible to ‘grow’ a SAS programmer from a non-programming background to an experience programmer. All of the classes, training, and projects you will work on are crucial in expanding your SAS knowledge and will allow you to have a very exciting career opportunity ahead of you.

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

 

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Project Plan: CDISC Implementation

Project Plan: CDISC Implementation

CDISC standards have been in development for many years. There are now methodologies and technologies that would make the transformation of non-standard data into CDISC-compliance with ease. Clinical trials have evolved and become more complex and this requires a new set of skills outside of clinical research – Project Management.

As with many projects, 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‘.

So what is CDISC? We can say that it is way of naming convention for XPT files, or field names naming conventions or rules for handling unusual data. Currently, there are two main components of CDISC: SDTM (Study Data Tabulation Model) and aDAM (Analysis Data Model).

As a project manager and with the right tool, you can look to a single source project information to manage the project through its life-cycle – from planning, through execution, to completion.

1) Define Scope: This is where you’re tested on everything that has to do with getting a project up and running: what’s in the charter, developing the preliminary scope, understanding what your stakeholders need, and how your organization handles projects.

The scope document is a form of a requirement document which will help you identify the goals for this project. It can also be used as a communication method to other managers and team members to set the appropriate level of expectations.

The project scope management plan is a really important tool in your project. You need to make sure that what you’re delivering matches what you wrote down in the scope statement.

2) Define Tasks: we now need to document all the tasks that are required in implementing and transforming your data to CDISC.

Project Tasks (Work packages) Estimates (work unit)
Initial data standards review 27
Data Integrity review 17
Create transformation models 35

The work breakdown structure (wbs) provides the foundation for defining work as it relates to project objectives. The scope of work in terms of deliverables and to facilitate communication between the project manager and stakeholders throughout the life of the project. Hence, even though, preliminary at first, it is a key input to other project management processes and deliverables.

3) Project Plan: Once we completed the initiation phase (preliminary estimates), we need to create a project plan assigning resources to project and schedule those tasks. Project schedules can be presented in many ways, including simple lists, bar charts with dates, and network logic diagrams with dates, to name just a few.

A sample of the project plan is shown below:

project plan sample

image from Meta‐Xceed paper about CDISC

4) Validation Step: Remember 21 CFR Part 11 compliance for Computer Systems Validation? The risk management effort is not a one-time activity on the project. Uncertainty is directly associated with the change being produced by a project.

The following lists some of the tasks that are performed as it pertains to validation.

  • Risk Assessment: Different organizations have different approaches towards validation of programs. This is partly due to varying interpretations of the regulations and also due to how different managers and organizations function. Assess the level of validation that needs to take place.
  • Test Plan: In accordance with the project plan and, if not, to determine how to address any deviation. Test planning is essential in: ensuring testing identifies and reveals as many errors as possible and to acceptable levels of quality.

test plan-cdisc

  • Summary Results: This is all the findings documented during testing.

An effective risk management process involves first identifying and defining risk factors that could affect the various stages of the CDISC implementation process as well as specific aspects of the project.

riskplan

5) Transformation Specification: Dataset transformation is a process in which a set of source datasets and its variables are changed to meet new standard requirements.

Some changes will occur during this step:

For example, variable name must be 8 chars long. The variable label must not be more than 40 chars in length. Combining values from multiple sources (datasets) into on variable.

6) Applying Transformation: This is done according to specification however, this document is active during the duration of a project and can change. There are now many tools available to help with this tasks as it could be time consuming and resource intensive to update the source code (SAS) manually. Transdata, CDISCXpres, SAS CDI, Define-it; just to name a few.

7) Verification Reports: The validation test plan will detail the specific test cases that need to be implemented to ensure quality of the transformation. For example, a common report is the “Duplicate Variable” report.

8) Special Purpose Domain: CDISC has several special purpose domains: CO (comments), RELREC (related records or relationship between two datasets) and SUPPQUAL (supplemental qualifiers for non-standards variables).

9) Data Definition Documentation: In order to understand what all the variables are and how they are derived, we need a annotation document. This is the document that will be included during data submission. SAS PROC CONTENTS can help in the generation of this type of metadata documentation.

The last step in the project plan for CDISC implementation is to generate the documentation in either PDF or XML format.

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.

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.

 

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How to write query texts – 6 template sentences

How to write queries unambiguously expressing what is asked for? Using short, polite sentences? Objectively explaining the underlying inconsistency?

First of all my general guidelines.

My preference is to use no more capitals then needed. Capitals in the middle of a query text, e.g. for CRF fields or for tick box options, could distract from getting the actual question asked. E.g. compare the same query texts, with and without extra capitals. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify Stop date. (Ensure that Stop date is after or at Start date AND that Stop date is not a future date.)
Referring to CRF fields as they are shown on the CRF. To easily find the involved field(s).
I prefer to leave any ‘the’ before a CRF field referral out of the query text. For more to-the-point query texts. E.g. compare the same query texts, with and without ‘the’ before data fields. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify the stop date. (Ensure that the stop date is after or at the start date and that the stop date is not a future date.)
Consistency in phrasing a query text can help to quickly write query texts or pre-program query texts in a structured, familiar way. That’s the thought behind the following 6 template sentences for query texts. Which you can use to help you write or program your queries.

The six ‘template’ sentences for query texts:

Please provide…

For asking the study site people to provide required data from patient care recordings. Examples: Please provide date of visit. Please provide date of blood specimen collection. Please provide platelet count. Please provide % plasma cells bone marrow aspirate. Please provide calcium result.

Please complete… For asking the study site people to complete required data as required by the study CRF design. (Not necessarily required for patient care). Examples: Please complete centre number. Please complete subject number. Other frequency is specified, please complete frequency drop-down list accordingly.
Please verify…

For asking the study site people to check date and time fields fulfilling expected timelines. Or for asking the study site people to check field formats. Examples: Please verify start date. (Ensure that start date is before date of visit.) Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify date of blood specimen collection. (Ensure that date of blood specimen collection is before or equal to date of visit and after date of previous visit.) Please verify date last pregnancy test performed. Please verify date of informed consent. (Ensure date of informed consent is equal to date of screening or prior to date of screening.) Please verify date as DDMMYYY.

…., please correct.

For asking the study site people to correct a data recording inconsistent with another data recording. Example: Visit number should be greater than 2, please correct.

…., please tick…

For asking the study site people to complete required tick boxes. Examples: Gender, please tick male or female. Pregnancy test result, please tick negative or positive. Any new adverse events or changes in adverse events since the previous visit, please tick yes or no. Laboratory assessment performed since the previous visit, please tick yes or no. LDH, please tick normal, abnormal or not done.

Please specify…

For asking the study site people to specify the previous data recording. Examples: Please specify other dose. Please specify other frequency. Please specify other method used. Please specify other indication for treatment.

Finally, for query texts popping up during CRF data recording, it could be helpful to put location information in it. Like: Page 12: Please verify start date. (Ensure that start date is after or at start date on page 11.)

Good luck finding your way to structure query texts…

Source:

This article is written by Maritza Witteveen of ProCDM. For clinical data management. You can subscribe to her blog posts at www.procdm.nl.”

 

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Data verification puzzles

 

Important part of the data management job is to verify received data. Checking for inconsistencies and unexpected patterns. Verifying that the data is complete, legible, logical and plausible.

However, how to perform data verification?

You could regard the data verification job as completing a couple of puzzles. Each puzzle is one subject participating in the clinical trial or clinical study at stake. As such, the puzzles resemble each other a great deal. But they are not exact copies. Each subject, each puzzle, is (slightly) different, unique.

Pleasant and thoughtful team action:

Do you have a puzzle somewhere in a cupboard? More than one from the same series? At least 2 puzzles with > 100 pieces each? Open the boxes, drop their content in one pile on the table and start completing the puzzles/subjects. The more pieces in place of a puzzle, the more evident which pieces to expect.

1. Get the parts received, divide them per subject/puzzle and start making all the puzzles. The clinical information up on each subject is coming in pieces, per completed visit data, per available adverse event information. In the beginning you’ll thus work with lots of incomplete puzzles.

2. Any holes in any puzzle/subject, any missing parts, you need to look for/query. Note that holes are allowed if your puzzle/story is as such! However, leave no unexpected holes. Meaning that if an assessment took place, you want to have the corresponding result(s) completed.

3. Any duplicate pieces, get rid of them. Please query.

4. Any pieces not fitting your puzzle/subject story, you need to check up on. Maybe they belong to another puzzle/subject. Or they are incomplete and can therefore not fit (yet). They could even be wrong delivered and not belong to the study at all.

5. Any pieces fitting but rotated 90 or 180 degrees, please turn/query. Get the puzzle showing a logical story.

6. Any pieces damaged, please try to fix the damaged parts. E.g. spilled coffee over a paper CRF. Illegible text parts. Or unclear texts that can be interpreted differently.

7. Any pieces added at the wrong place, query and bring to their right position. E.g. an error in an assessment date.

In trial/study language, the more data for a subject received and in the database, the easier to get the subject’s story complete. However, the more care needed to get the true story. The logical, plausible subject story. Attention to medication given for an adverse event but missing in the concomitant medication list. Or laboratory shifts to worse results but missing corresponding adverse events listed.

Completing the holes in a puzzle is easy, for data management the edit checks help you tremendously with that. Getting a logical, plausible story for each patient, reflecting the truth, is the real data management challenge. Which takes more than just structuring pieces. It asks you to look and understand the pictures up on the pieces received.

Good luck with your data management puzzles,

Good regards,

Source:

“This is an article of ProCDM. Clinical data management training. Receive tips and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing via http://www.procdm.nl/pages/knowledgebase.asp.”

 

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