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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 – Clinical Staffing and Resourcing

RAeClinica – Clinical Staffing and Resourcing

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

Source: RAeClinica – Clinical Staffing and Resourcing

 

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RAeClinica – Where Clinical Research meets Technology

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

Source: RAeClinica – Where Clinical Research meets Technology

 

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Team Collaboration and Conflict

Team Collaboration and Conflict

Conflict is inevitable and can be positive. Sometimes the person who disagrees actually does have a better way. Conflict is a natural result of change, but to manage it properly, we must focus on the facts, not the emotions. In other words; focus on the problem, not the person.

Ineffective team collaboration is one of the primary contributors to costly rework and delivery failure in many projects. Team collaboration is about sharing knowledge and reaching consensus within the team.

‘Problem-Solving Teams: Quality Circles’. I personally never read an article related to ‘conflict and team members with Quality Circles’ but they primary goal to foster an exchange of ideas and the use of basic tools such as brainstorming, checklists and Pareto chart, etc. were very familiar to me.

First, we should understand the major sources of conflict for a project. For instance, at the beginning of a project, project priorities, administrative procedures and schedules are the main sources of conflict. Towards the middle and end of a project, schedules create the most conflict, followed by resources, and technical issues. Personality conflicts are lower of the list, as are cost.

After we have clear understanding of what are the conflict and the sources, we can work on resolving those conflicts. Confronting the problem head-on without being confrontational towards the person is the best win-win situation. We examine alternatives with an open mind, and really agree on the best solution.

Ideally we want to build a positive relationship with positive statements all along. If you include a positive statement at the same time you address the problem, focus on the issue and be specific. For example, “I know it is not your fault but I trust that as a good team player, you will be here from now on.”

anayansi gamboa conflicts

In order to smooth the progress of conflict resolution, we should obtain feedback during the meeting and status reports; stress to the team and customer how critical it is to communicate any issues during the status meetings or at least to the project manager. Provide an explanation with the updated information.

Many projects do not deliver, and get canceled before they are completed. Team collaboration issues are very often the reason why projects fail, but if the right infrastructure is available to facilitate effective knowledge sharing among the team members, conflict will be minimize.

Source: {EDC Developer}

 

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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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Good Documentation Practice (GDP) for the EDC / SAS Developer

When writing programming codes for either validating the software or for validation checks, we often have to write comments to explain why we did something.

Since the FDA regulates computerized systems used in clinical trials under the authority of Title 21 the Code of Federal Regulations Part 11 (21 CFR Part 11) – see my other article about 21 CFR Part 11 here, we need to make sure our codes and programs are documented. As you have heard before, if it is not documented, it never happened. Nevertheless, there is no mandatory regulatory agency mandating to have to do this.

GDP is an expected practice”

So how much documentation is needed? We could get into endless discussions of when we should comment, what we should comment, and how much we should comment. I have had plenty of discussions about comments with people with various opinions on the subject.

Here’s a good documentation practice for a SAS code:

For more information please visit the original post at: {EDC} Developer

Need a clinical programmer, Data Programmers (Oracle/SAS/.NET) EDC Specialists (InForm, RDC, Rave)?

Contact Us!

 

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Data Management Plan in Clinical Trials

The preparation of the data management plan (DMP) is a simple, straightforward approach designed to promote and ensure comprehensive project planning.

This article is an extract from EDC Developer blog. Click here to read it. Data Management Plan in Clinical Trials.

RA eClinical Solutions provides clinical Data Management, Project Management and Technology Support within the Life Science community.

 

Source: EDC Developer

 

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A New Way to Collect Data – CDASH

There is a general consensus that the old paper-based data management tools and processes were inefficient and should be optimized. Electronic Data Capture has transformed the process of clinical trials data collection from a paper-based Case Report Form (CRF) process (paper-based) to an electronic-based CRF process (edc process).

In an attempt to optimize the process of collecting and cleaning clinical data, the Clinical Data Interchange Standards Consortium (CDISC), has developed standards that span the research spectrum from preclinical through postmarketing studies, including regulatory submission. These standards primarily focus on definitions of electronic data, the mechanisms for transmitting them, and, to a limited degree, related documents, such as the protocol.

Clinical Data Acquisition Standards Harmonization (CDASH)

The newest CDISC standard, and the one that will have the most visible impact on investigative sites and data managers, is Clinical Data Acquisition Standards Harmonization (CDASH).

As its name suggests, CDASH defines the data in paper and electronic CRFs.

Although it is compatible with CDISC’s standard for regulatory submission (SDTM), CDASH is optimized for data captured from subject visits, so some mapping between the standards is required. In addition to standardizing questions, CDASH also references CDISC’s Controlled Terminology standard, a compilation of code lists that allows answers to be standardized as well.

Example: Demographics (DM)

Description/definition variable name Format
Date of Birth* BRTHDTC dd MMM yyyy
Sex** SEX $2
Race RACE 2
Country COUNTRY $3

*CDASH recommends collecting the complete date of birth, but recognizes that in some cases only BIRTHYR and BIRTHMO are feasible.

  • *This document lists four options for the collection of Sex: Male, Female, Unknown and Undifferentiated (M|F|U|UN). CDASH allows for a subset of these codelists to be used, and it is typical to only add the options for Male or Female.

The common variables: STUDYID, SITEID or SITENO, SUBJID, USUBJID, and INVID that are all SDTM variables with the exception of SITEID which can be used to collect a Site ID for a particular study, then mapped to SITEID for SDTM.

Common timing variables are VISIT, VISITNUM, VISDAT and VISTIM where VISDAT and VISTIM are mapped to the SDTM –DTM variable.

Note: Certain variables are populated using the Controlled Terminology approach. The COUNTRY codes are populated using ISO3166 standards codes from country code list. This is typically not collected but populated using controlled terminology.

Each variable is defined as:

  • Highly Recommended: A data collection field that should be on the CRF (e.g., a regulatory requirement).
  • Recommended/Conditional: A data collection field that should be collected on the CRF for specific cases or to address TA requirements (may be recorded elsewhere in the CRF or from other data collection sources).
  • Optional: A data collection field that is available for use if needed

The CDASH and CDICS specifications are available on the CDICS website free of charge. There are several tool available to help you during the mapping process from CDASH to SDTM. For example, you could use Base SAS, SDTM-ETL or CDISC Express to easily map clinical data to SDTM.

In general you need to know CDISC standards and have a good knowledge of data collection, processing and analysis.

With the shift in focus of data entry, getting everyone comfortable with using a particular EDC system is a critical task for study sponsors looking to help improve the inefficiencies of the clinical trial data collection process. Certainly the tools are available that can be used to help clinical trial personnel adapt to new processes and enjoy better productivity.

 

Source: EDCDeveloper

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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