Posted by Keith ward on Fri, Oct 15, 2010 @ 09:27 AM
The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit group that defines clinical data standards for the pharmaceutical industry. CDISC has developed numerous data models. Four of these models are of particular importance:
Study Data Tabulation Model (SDTM). The SDTM defines the data tabulation data sets that are to be sent to the FDA as part of a regulatory submission. The FDA has endorsed the SDTM in its Electronic Common Technical Document (eCTD) guidance. The SDTM was originally designed to simplify the production of case report tabulations (CRTs), and therefore the SDTM is listing friendly, but not necessarily friendly for creating statistical summaries and analysis.
Analysis Dataset Models (ADaM). The CDISC ADaM standard defines data set definition guidance for the analysis data structures. These data sets are designed for creating statistical summaries and analysis. DZS/Clinplus has developed an extremely powerful SDTM to ADaM conversion tool to make this process much more user friendly. Please go to: http://www.clinplus.com/products/new-report-/ for more information.
Operational Data Model (ODM). The ODM is a powerful XML-based data model that allows for XML-based transmission of any data involved in the clinical trials. SAS has provided support for importing and exporting ODM files via the CDISC procedure and the XML LIBNAME engine.
Case Report Tabulation Data Definition Specification (Define.xml). Define.xml is the replacement for the data definition file (define.pdf) sent to the FDA in electronic submissions. Define.xml is based on the CDISC ODM model and is intended to provide a machine-readable version of define.pdf.Because define.xml is machine readable, the metadata about the submission data sets can be easily read by computer applications. This allows the FDA to work more efficiently with the data submitted to it. Exporting, importing, and creating data for these models is most important.
The FDA has begun to formally endorse the use of these data models in their guidance. Eventually the FDA will probably require data to be formatted to the CDISC model standards for all regulatory submissions.
There is also another blog article at: http://www.clinplus.com/-Data-and-MetaData-DZS-software/bid/29783/Need-to-convert-SDTM-data-to-ADaM-data-Here-s-How
Posted by Keith ward on Mon, Apr 12, 2010 @ 01:03 PM
The role of data presentation in the drug development lifecycle
New medicines are constantly being developed through a series of controlled trials which assess the safety and efficacy of each new medicine by applying high scientific standards. An experimental medicine is first tested in the laboratory and in animal studies. After this preclinical testing, the medicine can advance to clinical testing.
The data derived from clinical development
Clinical trials involve volunteer trial participants. To ensure such trials are conducted ethically, extensive rules and standards govern the trial design, investigator qualifications and training, external reviews by an institutional review board (IRB) or ethics committee, ongoing monitoring of all of the trial sites, and the obtainment of informed consent after the presentation of the risks and potential benefits of participation to the potential trial participant. Trial participants can withdraw from trials at anytime.
Phase 1 Trials
In Phase 1, an experimental medicine, also called an "investigational new drug," is administered for the first time to humans. Phase 1 clinical trials usually focus on safety and tolerability, rather than the effectiveness of a new medicine. Low doses of the experimental medicine are administered to a small number of participants under the close supervision of an investigator. Trial participants are typically healthy individuals, although for some medicines, the first trials in human participants are patients with the disease the experimental medicine intends to treat. Dosage of the new medicine is gradually increased during Phase 1 clinical trials to allow investigators to measure the participant's clinical response to the medicine, whether the medicine is sufficiently absorbed, how long the medicine remains in the bloodstream after dosing, and which dosage levels are safe and well-tolerated.
Phase 2 Trials
In Phase 2, the focus of the trials is on the effectiveness of an experimental medicine in treating an illness or medical condition. Information about the experimental medicine's safety, side effects, and potential risks is also collected. Researchers work to determine the most effective dosages for the experimental medicine and the most appropriate method of delivering it (e.g., tablets, extended release capsules, infusions, injections, etc.). Typically, Phase 2 clinical trials involve up to several hundred participants (although in some cases there could be fewer than 100). The participants studied in Phase 2 clinical trials are usually patients who have the medical condition that the experimental medicine is intended to treat. They are usually identified by physicians at research centers, clinics, and hospitals around the world.
Phase 3 Trials
Phase 3 trials test the results of earlier trials in larger populations and gather additional information about the effectiveness and safety of an experimental medicine. This phase usually involves several hundred to several thousand participants from multiple sites with many physician-investigators. These trials are often randomized and "double-blinded." "Double blinded" means that during the trial, neither the investigator nor the participant know who in the trial is receiving the experimental medicine versus a placebo (sugar pill) or another medicine (a "comparator"). Phase 3 trials generally provide the primary basis for the benefit-risk assessment for the new medicine and much of the core information about the medicine being analyzed for inclusion that will be described in the labeling of the medicine.
Registration
The next step in bringing a new medicine to market is the filing of an application with the health regulatory authority of a country in order to obtain approval to market the new medicine. This step is known as registration. In the U.S., a New Drug Application (NDA) and other application types are filed with the U.S. Food and Drug Administration (FDA). In Europe, a Market Authorization Application (MAA) is filed with the European Medicines Agency (EMA) for the evaluation of medicinal products.
Defined by the FDA as a "strategy to manage a known or potential serious risk associated with a drug or biological product," a risk evaluation and mitigation strategies (REMS) may be required as part of an NDA, abbreviated new drug application (ANDA), or biologics license application (BLA) when the agency deems it necessary to take additional steps to ensure the product's benefits outweigh its risks. The FDA can also require holders of approved applications to submit proposed REMS based on newly introduced safety information, such as previously unrecognized or unlabeled risks or new findings concerning a known serious adverse drug reaction.
A description of the medicine's manufacturing process along with quality data and trial results need to be provided to the health regulatory authorities to demonstrate the safety and effectiveness of the new medicine. If approval is granted, the new medicine can then be sold for use by patients. Medicines that have been recently approved for marketing in the U.S. or Europe are known as "recent approvals."
The critical role of data presentation
A major goal of biostatisticians and clinical data managers throughout the pharmaceutical industry is to implement consistent internal processes to maintain, manage, and monitor data in a manner compliant with regulatory agencies' requirements throughout the drug development lifecycle and beyond.
Simultaneously, teams of medical writers and programmers must produce accurate and consistent statistical tables and listings to present this data and their findings. Because they operate in a highly demanding and ever-changing environment, they must accomplish these goals while struggling with increasingly ambiguous regulations.
With the Clinical Data Interchange Standards Consortium (CDISC) setting out additional standards-the Study Data Tabulation Module (SDTM) and the Analysis Data Model (ADaM) -to support the derivation and calculation of the data, as well as recent emphasis on post marketing safety in the form REMS, the situation becomes even more daunting.
Phase 4 trials
These trials, also called "post-marketing studies," are conducted after the regulatory approval of a medicine. Through such trials, researchers collect additional information about long-term risks, benefits, and optimal use. These trials often involve thousands of subjects and may continue for years.
Recently, as part of the United States' regulatory approval process for new medicines, new uses for existing medicines, and the continuous monitoring of safety, companies may be required to conduct additional studies and testing to support the safety and effectiveness of their medicines. In the European Union (EU) companies can be required to conduct additional studies and testing to support the safety and effectiveness of their medicines or to carry out other defined activities at anytime in the lifecycle of a product. These studies, testing and other activities are referred to as post-marketing commitments (PMCs).
The creation of REMS as part of the Food and Drug Administration Amendments Act of 2007 (FDAAA) marked a significant moment in the regulation of drugs and biological products. FDAAA gives the FDA broad powers to control drug marketing and labeling, to require post-approval studies, to establish active surveillance systems, and to make clinical trial operations and results more visible to the public. In short, REMS is the process by which the FDA is going to require the establishment of effective post-marketing surveillance of all adverse events, drug-drug interactions, side-effects, and so forth-and there is no restriction on the range of products the FDA can address with this authority.
Just because a drug has been on the market for a decade or two, does not exempt it from surveillance by the FDA. Before requiring a post-market study, FDA must determine that adverse event reporting and post-market risk identification systems are insufficient to evaluate whether the drug can be distributed and taken safely. When these situations apply, the FDA requires sponsors submit a timetable for conducting the study, to provide periodic reports on the status of the study, including the number of participants enrolled, and the expected completion date.
FDAAA gives the FDA the discretion to require the sponsor to make any changes to the label the agency "deems appropriate to address the new safety information." Moreover, FDAAA confers authority to the FDA to level civil, monetary penalties if these post-approval studies are not conducted to the agency's satisfaction or are delayed.
In the near future, the FDA could set new guidance for the design of post-marketing studies to include assessing differences between subpopulations, e.g., sex, education, and health literacy, when determining strategies to mitigate risk. A one-size-fits-all approach to study design, particularly in light of developments in personalized medicine, may leave vulnerable patient populations underrepresented and conceal opportunities to mitigate risk. Accounting for biological differences between men and women or the special needs of patients with rare disorders where treatment options are limited, is critical to research and the development of treatment options. This approach avoids increased costs and the need for additional resources to conduct populations'-relevant, post-marketing studies. The evolution of REMS thus far raises a question of how broadly the FDA intends to require REMS and what these plans must include.
In essence, the process requires the FDA to use electronic medical records as the primary source of data to track all aspects of product safety (and probably efficacy as well). The records being covered here are not "billing-based," but are the actual patient charts. This is where worlds collide. The FDA will use EMRs to assess product safety post-marketing; they are also going to be using EMRs to assess outcomes-and both safety and outcomes are going to be compared with pre-approval data as well as comparing drugs against each other in ways no clinical trial ever could. There is no doubt drug safety labels will be modified as a result of these activities and the pharmaceutical industry will be required to support, and subsidize, these new activities.
How will pharma deal with providing this data?
Fortunately, by adhering to, and properly managing, data standards, intelligently organizing and presenting clinical data and post-marketing surveillance data among workgroups, departments, and across an enterprise to the FDA, organizations can dramatically improve time- and cost-savings, increase productivity, provide earlier visibility to reliable clinical data, and most importantly avoid disastrous product recalls.
For almost anyone involved in the life sciences industry over the past two decades, the widespread use of SAS® as the de facto standard for producing safety and efficacy data analyses, transforming and transporting data in clinical trials, and FDA submissions is well-recognized. Not quite as many however, are familiar with the SAS® report generator, "PROC Report," which is often mistakenly compared to the ClinPlus Report system. SAS® PROC Report procedures only offer summary statistics based on Proc Means and Proc Univariate, and multi-dimensional tables based on Proc Freq.
Generating tables and listings that intelligently and elegantly present these statistics and data can be a costly and extremely time-consuming undertaking, especially considering the salary of an advanced SAS® programmer. ClinPlus Report streamlines the entire process by enabling a broad range of users-often working in disparate locations around the world-to produce identically formatted tables and listings of the data with limited SAS® programming knowledge. It provides true authoring flexibility for any variety of statistical-based safety and efficacy tables and listings. This SAS®-based report authoring tool can create simple data listings as well as complex statistical summary tables. The point-and-click user interface makes it easy to operate and learn, and, it provides immediate access to data, significantly reducing the time needed create a new table or listing. Unlike other systems that simply generate stock reports, this powerful system empowers users with complete control over the "look and feel" of their templates, tables, and listings, and produces the highest quality output available in the industry.
The ClinPlus Report System can also produce many more summary statistics and inferential tests than PROC Report, in presentation quality, and in a more flexible way. Because of its ability to support the need to present validated data for risk evaluation and risk mitigations, ClinPlus Report is being used by most of the top 10 pharmaceutical organizations worldwide to support their need to gain early visibility to clinical trial data, prepare regulatory submission content, and support post-marketing epidemiologic initiatives.
Would you like to know more?
If you would like to know more about how ClinPlus Report can dramatically improve your existing process for programming and presenting data for clinical study reports, electronic submissions to regulatory agencies, or post-marketing safety and efficacy reports, please go to:
http://www.clinplus.com/ctms-landing-clinplus-clinical-trial-management-system-ctms/?utm_campaign=ClinPlus-Report&utm_source=Blog
Posted by Keith ward on Fri, Oct 30, 2009 @ 03:30 PM
Introduction
Wondering how you can effectively meet the clinical data study standards set out by the Clinical Data Interchange Standards Consortium (CDISC), specifically regarding the Study Data Tabulation Module (in a process also known as SDTM mapping) and the Analysis Data Model (ADaM)? Then our data conversion and reporting tool may be the solution for which you’ve been looking. Imagine being able to generate all of your safety tables and listings, and provide supporting documentation in a validated, standardized ADaM-compliant manner. Now you can.
Background
Adhering to CDISC standards has proven to facilitate efficient data integration and transport, as well as access and review. Ideally, FDA Reviewers will want to perform analytic review using ADaM standards. However, converting your SDTM mapping data to ADaM data, to submission-ready tables, and then accurately documenting the process can present numerous challenges.
One of the primary challenges is providing complete documentation of the creation of the tables and listings, and the underlying ADaM data sets, the process by which each variable in the ADaM is traced back to its original source. CDISC specifications indicate that any variables copied or derived from an SDTM domain into an ADaM data set must retain the integrity of the data in the SDTM domain.
The ClinPlus Solution
ClinPlus has developed an elegant solution for metadata driven conversion of SDTM mapping data to ADaM data, and then produces metadata parameterized- driven flexible safety tables and listings from the ADaM data. The metadata defined in Microsoft Excel spreadsheets becomes the ADaM documentation, thereby eliminating any chance that documentation might not correspond to the actual process.
Data Conversion
The first step is to convert your SDTM data to your ADaM data using the ClinPlus Data Converter Method. The ClinPlus Data Converter Method is controlled by metadata stored in Microsoft Excel spreadsheets that define how SDTM mapping data are written into ADaM data structures in an Excel worksheet for each data domain. A data conversion rule is specified for each variable. These conversion rules are written using Base SAS programming syntax that is submitted to SAS during the conversion process. A cross-reference to Statistical Analysis Plan documents the rational for each conversion or derivation. This method provides clean, exact documentation of the process, as well as simple maintenance and versioning. This approach not only documents a complicated process, but maintains the data integrity between the SDTM and ADaM data sets, thus meeting CDISC specifications.
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