Category Archives: Data Management

How the Pharmaceutical Industry became into existence?

The Birth of the Pharmaceutical Industry can be traced back to 1850 – 1875 where the first “authentic” drug was developed by extracting agents from plants.

In those years, there were also developed the microbial theory of disease, medicines and homoeopathy patent and there heavy use of powerful purgatives and cathartics medicines.

By 1875, the Drug Development becomes a science and the first synthetic drug was introduced. By 1900, vaccines and antitoxins form the basis of the new pharmaceutical industry.

From 1900 to 1925, began what is now known as the pharmaceutical century. The U.S. Pure Food and Drugs Act was passed (known today as the FDA) and the development of hormonal and chemotherapy was introduced.

After 1925, it began what is known today as the Antibiotic and Regulatory Era. Vitamins, antimalarials, anticarcinogenic compounds and anti-infectives discoveries were made. The FDA gains independence as a regulatory agency and is given compliance responsibility.

From 1950 thru 1975, a new generation of the drug became apparent. Vaccines for polio became mandatory, oral contraceptives appears on the market and cardiovascular therapies.

Also, an amendment to the Federal Food Drug and Cosmetic Act of 1938 Act was signed by President Kennedy to ensure that consumers will not be the victims of unsafe and ineffective medications. Additional information about this Act can be read here: Kefauver-Harris Amendments

The Globalization of the Pharmaceuticals industry started from 1975 thru 2000. New drugs therapies, new antiviral drugs, and the development of new drugs by biotechnology companies were in global high demand.

By 2000, over 2.8 million people participated in over 50,000 clinical trials and more research and development funding was increased.

R&D Expenditures by Body System

R&D Expenditures

Cost (Billions) Body System
7.0 Central Nervous System
6.0 Cancer, endocrine and metabolic
4.5 Cardiovascular
3.5 Infectious Disease
2.5 Biological and vaccines
5.3 Other

Current Trends in the Industry since the late 2000s

Biotechnology Medicines
Drug Intervention Targets
Vaccines

 

Role of Generic Drugs
Generic drugs account for over 47% of prescription drugs in the market in which 43 major prescription drugs come off patent by 2005.

What does this mean for pharmaceutical drugs?
Between 40% – 60% of sales are lost to the generic brand within the two years of coming off patent.

Factors that will impact the R&D future:

  • There has been over 40 mergers and acquisitions since 1985
    • Sanofi and Synthelabo
    • Zeneca and Astra
    • Monsanto and Pharmacia
    • Roche and Genentech
  • Employment grew at a 3% rate per year

Total Drug Development Time (Years)

Summary:
On average, it takes at least ten years for a new medicine to complete the journey from initial discovery to the marketplace, with clinical trials alone taking six to seven years on average. The average cost to research and develop each successful drug is estimated to be $2.6 billion.

Pharmaceutical Regulation

In 1902, about 10 children die after receiving a vaccine shot. The BIOLOGICS CONTROL ACT was passed to ensure purity and safety of serums, vaccines and similar products used to prevent or treat diseases in humans.

In 1906, “The Great American Fraud” reveals patent medicines laced with addictive drugs, toxic additive and alcohol.

In 1912, Public outcry over the sales of “snake venom” and other wonder cures.  The SHERLEY AMEND was passed to prohibit labelling medicines with false therapeutic claims intended to defraud the purchaser.

In 1927, the government forms a separate law enforcement agency called the Food, Drug and Insecticide Agency.

In 1937, about 107 people, including many children die after drinking a syrup called Elixir of Sulfanilamide. Due to safety concerns, The Federal, Food, Drug and Cosmetic Act (1938) was passed as the first attempt to regulate cosmetics and medical devices.

In 1962, the sleeping pill “Thalidomide“, developed what is known today as Grunenthal Pharmaceutical in Germany, resulted in thousands of birth defects in Western Europe. The KEFAUVER-HARRIS Drug Amend was passed to ensure drug efficacy and greater drug safety.

In 1983, the ORPHAN DRUG Act was passed enabling the FDA to promote research and marketing of drugs needed for treatment of rare diseases. This year there as an outbreak of HIV / AIDS cases.

In 1988, The FOOD and DRUG ADMINISTRATION ACT were established as the FDA under the Department of Health and Human Services.

In 1992, the pharmaceutical industry complained that life-saving drugs were being reviewed too slowly by the FDA. The PRESCRIPTION DRUG USER FEE ACT (PDUFA) is made into law.

Source:

Image: Courtesy of Google image

FDA website

Private information for college researched in the 2000s.

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Becoming a Data Scientist {EDC Developer + Statistical Expert + Data Manager}

At an early age, I was drawn to computers. I did well in math; I love science and I started enjoying programming when my stepfather gave me a small computer to program games. This was my real experience with programming. I think the programming language was Basic. The computer had some built-in games and basic math problems in it but you could also play around with ‘Basic‘ codes and create your own.

Then I went to a technical school and into college where you take basic classes in information system /technology and took courses in telecommunication management.  Most of the courses were around IP, PBX and Network Administration.  As part of that curriculum, I took a basic programming course and VB.net. I really like that since it has a visual interface (drag and drop to create the interface) and when you click a button you create an event so I like the design aspect of it (I am known to be very creative) then I started to design for people (website design and development, small databases). A lot better than working in telecommunications. I thought VB was a great first language to learn. Later I took a Microsoft Access database development class and we learn database design (relational) and found out I was really good at that.

Before I graduated, I was already working for a well known pharmaceutical company as a database analyst within their data management and biometrics team. They really like what I did with their clinical operations data (investigator data – you know the one that now we need CTMS systems for nowadays). So this was a confirmation that ‘databases’ was my passion. I love designing it, managing and maintaining it.

During my early years in this industry, I spent a lot of time writing SQL codes and SAS programs.  We pulled the messy data (back in those years we used the Clintrial Oracle backend system) and very problem solving oriented. A business question was asked and we would go using either SQL or SAS and go into this messy database and figure it out the answer. I really enjoyed that.

In recent years, I take data from a {EDC} system then write scripts to summarize the data for reporting and put into a data warehouse and then I use a product called ‘IBM Cognos’, which points to the data warehouse to build those reports and worked with different users across different departments (a lot of different audiences for the data) with a lot of different interesting data in there. I have spent time using APIs to extract data via Web Services (usually in XML-ODM format) and generate useful reports in SAS or Excel XML.

People think that being a data analyst is just sitting around a computer screen and crunching data. A lot of it is design-oriented, people-oriented, and problem-solving. So when people ask a question, I get to dive into the data and figure it out the answer.

Next step is to get into predictive analytics and do more data mining and data forecasting.

Are you still excited about becoming a data scientist?

You can start by reading my blog about programming languages you should learn here!

Other tools and programming languages you should learn: Anaconda, R Programming, Python, Business Intelligence Software like Tableau, Big Data Analytics with Hadoop, create new representations of the data using HTML and CSS (for example when you use APIs, XML to extract data from third-party sources).

Anayansi, MPM, an EDC Developer Consultant and clinical programmer for the Pharmaceutical, Biotech, and Medical Device industry with more than 18 years of experience.

Available for short-term contracts or ad-hoc requests.  See my contact page for more details or contact me.

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EMA and The Netherlands Biopharm Opportunities

The European Medicines Agency (EMA) is relocating from London to Amsterdam.

According to EMA, the Seat Agreement allows EMA to function independently in the Netherlands. Similar agreements apply to other EU agencies located in the Netherlands.

On Friday, 13 April 2018, the Dutch Council of Ministers agreed to sign the document.

Amsterdam – EMA

Quantify research reported earlier this year that the shift of the EMA to Holland will be a welcome boon to that country, particularly due to some companies shuttering their Netherlands locations in favor of other European countries. The research report pointed to more than 1,500 job losses about eight years ago following Abbott and Merck Sharpe and Dohme closing facilities in that country. Between 2009 and 2014, Quantify said the Dutch market share in the European pharma industry plummeted from 3.3 percent in 2009 to 17 percent in 2014.

To help spur the resurgence of the Netherlands pharma industry, the Netherlands Foreign Investment Agency is hosting a landscape tour at the end of summer to showcase the country’s offerings. While the EMA is expected to become the core of the Netherlands biopharma industry, the NFIA said the country is home to facilities for more than 420 biopharmaceutical companies, such as AstraZenecaJanssen, MSD, Amgen and Teva, to name a few. The Netherlands Foreign Investment Agency pointed to the companies because they “rely on their Dutch operations for both R&D and distribution activities.”

But, it’s not just the large global pharma companies that have a home in Holland. The country is also home to a number of biopharmaceutical startups and scale-ups, like GalapagosGenmabPharming and uniQure.

Another company that will be relocating to the Netherlands is Gilead Sciences.  This new facility will be employing over 300 people. Other companies worth mentioning are GSK-Novartis, Merk and most well-known Clinical Research Organizations (CROs) have made the Netherlands their home.

For those interesting in working and living in Amsterdam, you should know that it is one of the countries with the highest taxes at a 52% income tax rate if you make over 55,000 euros per year (apparently they think this is good money). For a regular employee with benefits, your tax rate will be at a 42 %. Don’t expect to be making more than 55,000 euros a year unless you are a highly skilled employee or hold a managerial level position.

Tot Ziens!

Source:

Biospace

EMA

https://www.government.nl/latest/news/2018/04/23/the-european-medicines-agency-ema-and-the-netherlands-agree-on-seat-agreement

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CTCAE: Common Terminology Criteria for Adverse Events

The National Cancer Institute issued the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 on November 27, 2017.

So what is CTCAE and what is it used for?

The terminology NCI CTCAE is a descriptive terminology that can be used for the declaration of adverse events (AEs). A grade scale (or severity) is provided for each term.

The oncology community has a standard classification and severity grading scale for adverse events in cancer therapy clinical trials and this is what it is described in the CTCAE reference.

The SOC (System Organ Class or Organ Class) is the highest level of the hierarchy of the
MedDRA dictionary. It is identified by a physiological or anatomical classification, etiological or a result (ex: SOC investigations for laboratory results). The terms of the CTCAE are grouped together according to the MedDRA primary SOCs. Within each SOC, the terms are listed and accompanied a description of the severity (grade).






An adverse event is an unexpected sign, symptom or disease, unexpected (this includes
biological results), associated chronologically with the use of a treatment, a procedure,
to be connected to this treatment or procedure. An IE is a unique term representing an event
specifically used for the medical report and the scientific analyzes. Each term of the CTCAE is a
MedDRA LLT level term (Low Level Term, lowest level of the hierarchy).
Grades refer to the severity of AEs. The CTCAE is divided into 5 grades, each with
unique medical description for each term, based on the following main lines:
Grade 1: Light; asymptomatic or mild symptoms; diagnosis on clinical examination only; born
not requiring treatment
Grade 2: Moderate; requiring minimal, local or non-invasive treatment; interfering with activities instrumentalities of everyday life
Grade 3: Severe or medically significant but without immediate life-threatening;
indication of hospitalization or prolongation of hospitalization; invalidating; interfering with activities elementary of everyday life
Grade 4: Life-threatening; requiring emergency care
Grade 5: Death related to AE and it is not appropriate for some AEs and therefore is not an option.
MedDRA code CTCAE v5.0 Term Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Definition
10007515 Cardiac arrest Life-threatening
consequences; urgent
intervention indicated<
Death A disorder characterized by cessation of the pumping function of the heart.

CTCAE is still the formal reporting for AEs and grading dependent upon clinician judgement of medical significance.

A copy is located here: CTCAE version 5.0.

Sources:

https://ctep.cancer.gov/protocolDevelopment/electronic_applications/docs/CTCAE_v5_Quick_Reference_8.5×11.pdf

Feature image: CTCAE-4 by Stefano Peruzzi (apple app)

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Got Medrio? The Next Best EDC…

Medrio is a low cost solution that offers easy mid-study                changes and intuitive phase I workflows.

Medrio

One of my favorite features of Medrio is the Skip logic functionality. So what is Skip logic?

Let’s demonstrate this feature by using the Demography form / Race field:

In many EDC systems that I am currently using or used in the past, we have to create separate fields for each option and write a custom edit check to flag when data has been entered under the specify field. This scenario request data on the specify field if the OTHER race option is checked but with skip logic, no other option will be allowed to enter data (e.g. White or Black or Indian) if the user did not select OTHER as an option and the required field ‘Specify’ is made visible and available (mandatory) for data entry.

Medrio

eCRF – DEMO – Medrio

 

 

 

 

 

 

 

 

DM form – Skip Logic

 

 

 

 

 

 

 

In the above screenshot,  the query resulting from the skip logic configuration if OTHER specify is not completed. In other words, when Race other than ‘OTHER’ is checked, the specify field will be skipped (not enterable). To make this work and as a best practice, you will need to make the ‘OTHER’ field required during data entry.

If you are looking for a study builder or clinical programmer to support your clinical trials and data management department, please use the contact form.

Source: medrio.com

Disclaimer: The EDC Developer blog is “one man’s opinion”. Anything that is said on the report is either opinion, criticism, information or commentary, If making any type of investment or legal decision it would be wise to contact or consult a professional before making that decision.

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Hashgraph: Electronic Data Capture Future?

Hashgraph technology, created by Leemon Baird, it is probably going to replace blockchain technology.

Since the introduction of bitcoin there have been many thousands of blockchain based crypto currencies created and there are more being created every single day so what’s different about hashgraph?

Well it isn’t a blockchain it’s totally different in fact the way it works is a real Mindbender and a bit difficult to explain.

Instead of a block in a blockchain hash graph calls their packages of information “events”. Your computer takes a transaction like a payment or anything else for that matter such an action in the eCRF form (e.g. SDV) you want to record and puts it in the event for transmitting information quickly hash graph uses a technology that has been the gold standards in computer science for decades. Its super fast and its called ‘gossip protocol’. Your computer randomly tells another computer in the network about the even you’ve created and that computer responds by telling your computer any events it heard about then that computer tells another computer about your event and the other events it heard about and the computer its talking to responds by telling all the events its knows about. Its absolutely the best most efficient way to spread information and it’s exponentially fast  And the best part, it also includes the information of the time it heard it and who it heard it from and the time they heard it and who they heard it from and so on and it is called ‘gossip about gossip’ and it lets everyone knows what everyone else knows and exactly when they knew it and just fractions of a second.

Another key feature is ‘virtual voting’. Even though an old technology, it was slow but with hash graph, there is no voting instead because everyone already knows what everyone else knows you can mathematically calculate with 100% certainty how they would vote and allows hash graph to come to consensus almost instantly so instead of recording things on a block and adding it to the block chain once every 10 minutes hash graph events are added to the system instantly the moment they are created so they don’t have 10 minutes worth of information in them. That means they are small and contain far less data so they use very little bandwidth and are much easier to transmit and uses a minuscule amount of power which makes it fast, fair and more secure than block chain.

All events are time-stamped the moment they are woven into the system so the record of whose event came first and whose came second is instant and there is no such thing as soft forking or unconfirmed events.

It can also replace huge portions of the internet that are currently run by centralized servers by replacing them with the shared computing power of all of our own computers, iPads and cell phones.

It looks like hash graph might have all the potentials  of fulfilling all the original hopes and dreams of a true Electronic Data Capture system (e.g. eCRF forms collected at site, ePRO/eCOA data directly from subjects, external or local lab or ECG data from any lab, eSAEs, Inform consents, and more). In other words, sites, sponsors, labs, regulatory and all vendors working seamless with each other.

Imagine an investigator or research site completing an ‘event’ (e.g. Enrolled or randomized) and the system automatically sent the payment to the site at the end of each event.

The power to decentralize and remove the middleman with the speed at which technology is evolving the future is looking bright.

One of the challenges of any new technology is how do you really explain to people how do you make a compelling case for what makes a technology so compelling and one of the things that is so compelling about this technology is throughput – the speed.

You are probably thinking but what about eSignatures? Or Informed Consents? or Regulations? There is another technology ‘smart contracts‘ that could lead to substantial improvements in compliance, cost-efficiency and accountability.

What is a  Smart contracts? are contracts whose terms are recorded in a computer language instead of legal language. Smart contracts can be automatically executed by a computing system, such as a suitable distributed ledger system. The potential benefits of smart contracts include low contracting, enforcement, and compliance costs; consequently it becomes economically viable to form contracts over numerous low-value transactions. The potential risks include a reliance on the computing system that executes the contract. [Distributed Ledger Technology: beyond block chain, UK Government Office for Science, 2006]

Smart contracts

Bitcoin technology uses a tremendous amount of energy to run the system and also it is scaled up it became slower and slower to where it was no longer a currency it was basically a speculative vehicle you could make some gains in purchasing power. A transaction with bitcoin technology can take 4 hours for confirmation.  With hashgraph, compared to Bitcoin, uses no power.

If Bitcoin were to replace the entire world monetary system and financial markets, it would use more power than the entire world produces. It’s completely unsustainable.

Hashgraph, smart contracts, distributed ledgers and similar technologies offer new ways to share information, reduce errors, and it is cost effective to all users. Perhaps a new Electronic Data Capture system for clinical research will emerge.

Source:

Hashgraph.com

The Crypto Revolution

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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

 

Understanding Audit Trail Requirements in Electronic GxP Systems

Computerized systems are used throughout the life sciences industry to support various regulated activities, which in turn generate many types of electronic records.  These electronic records must be maintained according to regulatory requirements contained within FDA’s 21 CFR Part 11 for US jurisdictions and Eudralex Volume 4 Annex 11 for EU jurisdictions.  Therefore, we must ensure the GxP system which maintains the electronic record(s) is capable of meeting these regulatory requirements.

What to look for in Audit Trail?

  • Is the audit trail activated? SOP?
  • Record of reviews? (most companies trust the electronic systems audit trail and generates electronic paper version of it without a full review)
  • How to prevent or detect any deletion or modification
    of audit trail data? Training of staff?
  • Filter of audit trail

Can you prove data manipulation did not occur?

Persons must still comply with all applicable predicate rule requirements related to documentation of, for example, date (e.g. 58.130(e)), time, or sequencing of events, as well as any requirements for ensuring that changes to records do not obscure previous entries.

Consideration should be given, based on a risk assessment, to building into the system the creation of a record of all GMP-relevant changes and deletions (a system generated “audit trail”).

Audit trail content:

Audit trail content and reason it is required:
Identification of the User making the entry This is needed to ensure traceability.  This could be a user’s unique ID, however there should be a way of correlating this ID to the person.
Date and Time Stamp This is a critical element in documenting a sequence of events and vital to establishing an electronic record’s trustworthiness and reliability.  It can also be effective deterrent to records falsification.
Link to Record This is needed to ensure traceability.  This could be the record’s unique ID.
Original Value  

This is needed in order to be able to have a complete history and to be able reconstruct the sequence of events

New Value
Reason for Change This is only required if stipulated by the regulations pertaining to the audit trailed record.  (See below)

FDA / Regulators findings and complaints during Inspection of Audit Trail Data:

  • Audit User sometimes is hard to describe (e.g. user123 instead use full names of each user IDs thus requirement additional mapping)
  • Field IDs or Variables names are used instead of SAS labels or Field Labels (map field names with respective field text (e.g.  AETERM displayed instead use Reported Term for the Adverse Event)
  • Default values should be easily explained or meaningful (see annotated CRF)
  • Limited access to audit trail files (many systems with different reporting tools or extraction tool. Data is not fully integrated. Too many files and cannot be easily integrated).
  • No audit trail review process. Be prepared to update SOPs or current working practices to add review time of audit trails. It is expected that at least, every 90 days, qualified staff performed a review of the audit trail for their trials. Proper documentation, filing and signature should be in place.
  • Avoid using Excel or CSV files. Auditors are now asking for SAS datasets of the audit trails. Auditors are getting trained to generate their own output based on pre-defined set of parameters to allow auditors to summarize data and produce graphs.
  • Formatting issues when exporting into Excel, for example.  Numbers and dates fields change it to text fields.
Audit Trail Review

What data must be “audit trailed”?

When it comes to determining on which data the audit trail must be applied, the regulatory agencies (i.e. FDA and EMA) recommend following a risk based approach.

Following a “risk based approach”

In 2003, the FDA issued recommendations for compliance with 21 CFR Part 11 in the “Guidance for Industry – Part 11, Electronic Records; Electronic Signatures — Scope and Application” (see reference: Ref. [04]).  This guidance narrowed the scope of 21 CFR Part 11 and identified portions of the regulations where the agency would apply enforcement discretion, including audit trails. The agency recommends considering the following when deciding whether to apply audit trails:

  • Need to comply with predicate rule requirements
  • Justified and documented risk assessment to determine the potential effect on product quality
  • product safety
  • record integrity

With respect to predicate rule requirements, the agency states, “Persons must still comply with all applicable predicate rule requirements related to documentation of, for example, date (e.g., § 58.130(e)), time, or sequencing of events, as well as any requirements for ensuring that changes to records do not obscure previous entries.”  In the docket concerning the 21 CFR Part 11 Final Rule, the FDA states, “in general, the kinds of operator actions that need to be covered by an audit trail are those important enough to memorialize in the electronic record itself.” These are actions which would typically be recorded in corresponding paper records according to existing recordkeeping requirements.

The European regulatory agency also recommends following a risk based approach.  The Eudralex Annex 11 regulations state, “consideration should be given, based on a risk assessment, to building into the system the creation of a record of all GMP-relevant changes and deletions (a system generated “audit trail”).”

MHRA Audit

When does the Audit Trail begin?

The question of when to begin capturing audit trail information comes up quite often, as audit trail initiation requirements differ for data and document records.

For data records:

If the data is recorded directly to electronic storage by a person, the audit trail begins the instant the data hits the durable media.  It should be noted, that the audit trail does not need to capture every keystroke that is made before the data is committed to permanent storage. This can be illustrated in the following example involving a system that manages information related to the manufacturing of active pharmaceutical ingredients.  If during the process, an operator makes an error while typing the lot number of an ingredient, the audit trail does not need record every time the operator may have pressed the backspace key or the subsequent keystrokes to correct the typing error prior to pressing the ‘‘return key’’ (where pressing the return key would cause the information to be saved to a disk file).  However, any subsequent ‘‘saved’’ corrections made after the data is committed to permanent storage, must be part of the audit trail.

For document records:

If the document is subject to review and approval, the audit trail begins upon approval and issuing the document.  A document record undergoing routine modifications, must be version controlled and be managed via a controlled change process. However, the interim changes which are performed in a controlled manner, i.e. during drafting or review comments collection do not need to be audit trailed.  Once the new version of a document record is issued, it will supersede all previous versions.

Questions from Auditors: Got Answers?

When was data locked? Can you find this information easily on your audit trail files?

When was the database/system released for the trial? Again, how easily can you run a query and find this information?

When did data entry by investigator (site personnel) commence?

When was access given to site staff?

Source:

Part of this article was taking, with permission, from Montrium – Understanding Audit Trail Requirements in Electronic GXP Systems

Fair Use Notice: Images/logos/graphics on this page contains some copyrighted material whose use has not been authorized by the copyright owners. We believe that this not-for-profit, educational, and/or criticism or commentary use on the Web constitutes a fair use of the copyrighted material (as provided for in section 107 of the US Copyright Law).

Top 3 Posts at (EDC Developer)

Fist, I would like to thank everyone who has read articles posted at {EDC} Developer. Especially, my colegas and friends from India. The highest reading and hits have come from people living in India.

New to the industry? Want to get in as clinical data manager or clinical programmer? Looking for a particular topic or an answer to a question? check the contact me section.

Here are the top most searched articles this past few months:

1- Data Management: Queries in Clinical Trials

2- How to document the testing done on the edit checks?

3- Why use JReview for your Clinical Trials?

Others most read articles:

Role of Project Management and the Project Manager in Clinical Data Management

4 Programming Languages You Should Learn Right Now (eClinical Speaking)

Data Management Plan in Clinical Trials

For the search term used to find {EDC} Developer:

1-types of edit checks in clinical data management

2-Rave programming

3- pharmaceutical terminology list

4-seeking rave training (better source is mdsol.com)

5- edc programmer

6-central design tips and tricks

Thank you for reading!

Data Management Plan – Coding and Reconciliation

All Adverse Events and Previous/Concomitant Medication should be coded and/or approved prior and during the trial.

Before adverse event terms can be reported or analyzed, they must be grouped based on their similarities. For example, headache, mild headache and acute head should all be counted as the same kind of event. This is done by matching (or coding) the reported adverse events against a large codelist of adverse events which is also known as dictionary or thesaurus.

Test cases and other documentation associated with the testing of auto-coding should be produced/documented.  This documentation is not part of the plan. It is a product of the design process and should be filed separately in the TMF system.

In the DMP. you should document the variables and the dictionary to be used.

For Concomitant Medications, WHO drug reference list is used.  Also document the version used and if applicable, the final version of the who drug (for trials running over 6 months).

For Adverse event, MedDRA dictionary is the choice of coding method. Document the version used.

Serious Adverse Event (SAE) Reconciliation:

Indicate SAE Reconciling Approach to be used to compare SAE database (e.g. Argus) to the Clinical study| database (e.g. EDC):

  • Indicate tools to be used
  • Location of SAE data
  • Planned timing
  • Planned frequency of SAE Reconciliation activities

What to look for during reconciliation:

  • There are matched cases but minor differences such as onset date
  • Case found in the CDMS but not in the SAE system
  • Case found in the SAE system but not in the CDM system

Methods for Reconciliation:

For electronic-automatic reconciliation between systems, there are some challenges you need to identify first such as which type of data is to be reconciled and then which fields to compare. Best practice is to reconciled those considered serious according to regulatory definitions.

For manual reconciliation, reports such as SAS listings extracted from both systems with study information, subject or investigator and other key data can be used to perform manual review.  A manual comparison of the events can then assure that they are both complete and comparable.

Central Coding Anayansi Gamboa
Central Coding

No matter which method you used for reconciliation, each type of data (eg, AE, MedHist, Conmed) should document which glossaries and version were used.

When data from the clinical trial database is entered into a drug safety database for coding, the data between the two systems should be reconciled to verify the data in both systems are

identical. The processes and frequency of reconciliation should be specified.

Source:

DIA -A Model Data Management Plan StandardOperating Procedure: Results From

the DIA Clinical Data Management Community, Committee on Clinical Data Management Plan

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“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

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, Medrio, IBM eCOS, OpenClinica Open Source and Oracle Clinical.