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Case Study 4: A Full Data Management Solution

Case Study 4: A Full Data Management Solution

Working in a Collaborative Environment

The Scenario:

A phase II study was being managed by a CRO that had non-dedicated teams, escalating costs, with project timelines slipping on almost every deliverable.
RA eClinica Solution:

    • RA eClinica assumed responsibility for entire data management activities consisting of Data Management, Study Build / EDC Development, and Statistics and Programming.
    • RA eClinica preferred Data Management systems utilized with Sponsor’s Safety Surveillance system and Clinical Trial Management System, CTMS

Ra eClinica Results:

    • Study ongoing – All deadlines to date have been met or exceeded
    • Cost savings of approximately 35% in comparison to traditional CRO models
    • No turnover since study start

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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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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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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