Tag Archives: data collection

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?

Anayansi Gamboa - Data Status

 

 

 

 

 

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.

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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.


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.