Category Archives: DMP

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

 

Advertisements

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

-FAIR ;USE-
“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.

Data Management Plan – Study Specific Documents

Data Management personnel are responsible for creating, collecting, maintaining and/or retaining all essential study documents when contracted by the sponsor (e.g. biotech company, big pharma client).

It is important to keep electronic and paper records or hard-copies and specify retention records of these essential documents:

  • Final version including amendments of the clinical protocol
  • Final version of the CRF/eCRFs
  • Final version of the completion guidelines
  • All final approvals and written authorization (e.g. emails or note to files).
Study Specific Anayansi Gamboa
Study specific

-FAIR ;USE-
“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.

Data Management Plan – Database Archive

Indicate how you intend to archive and share your data and why you have chosen that particular option.

The DMP should outline specific information regarding the organization’s procedures for archiving the electronic records.

Good practice for digital preservation requires that an organization address succession planning for digital assets.

Which criteria will you use to decide which data has to be archived? What should be included in the archive?

Type of data (raw, processed) and how easy it is to reproduce it. Also consider archiving audit trails as long as the records are (CRF Part 11, Section 11.10).

Does the archive have specific requirements concerning file formats, metadata etc.

It is recommended to use open source formats such as PDF-PDF/A, ODM-XML or ASCII type of files.

anayansigamboa

 

 

 

 

Who is responsible for the data after the project ends?

Sponsor, CRO, Vendor? All should be documented on the DMP. Once database is locked, within a reasonable time and after data submission to a regulatory agency, you want to archive your database for long term storage and recovery.

While most data submitted to regulatory agencies are available in SAS formats, there may be times when going back to the original data format may be required.

Even though the easiest way to make sure data is available after database lock is to archive this data in the built in structure as the current system. For example, for Medidata Rave studies, trials are built on on top of SQL server, hence, you should consider archiving the old studies in a compatible format of SQL Server, without any transformation or data manipulation = raw data.

Other formats for data archive can be considered are ODM XML, PDF-PDF/A or ASCII A-8. These are some options for long=term storage. FDA says in the guidance document for 21 CFR Part 11, ‘scope and application – section C.5″, “FDA does not intend to object inf you decide to archive required records in electronic format to nonelectronic media….As long as predicate rule requirements are fully satisfied and the content and meaning of the records are preserved and archived, you can delete the electronic version of the records”.

Archival Plan

For archiving data, this plan should list all the components of the orginal system that will be included in the archive and the formats being used for their storage.

The best practices for clinical data archiving in clinical research are no different from those for archiving any other kind of industry.

 

 

-FAIR ;USE-
“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, OpenClinica Open Source and Oracle Clinical.

Data Management Plan – Protocol Summary

This usually describes the management plan for the data collected  during the project. It is a brief description or synopsis of  the protocol.

The protocol, in terms of a clinical research study, is the plan, or blueprint, that
describes the study’s objectives, methodology, statistical considerations, and the organization of the study. [CDISC.org Oct. 2012]

Protocol Summary
Protocol Summary – current state of ‘standardization’ of a protocol document

 

 

 

 

 

 

 

 

 

 

 

 

 

What to look for when reading a protocol?

  • Review of T&E – Time and Event Schedule or Visit Schedule.
  • Assessments e.g. ECGs, PE (physical exams), MH-MedHix or Medical HIstory, labs and more.
  • Critical data variables for analysis. e.g. efficacy and safety data

 

proc print data= work.demog;
where patient in(“&pid”) and page=’3′;
var patient SBJINT page
dob sex bmi weight height;
title ‘Page 3 – Demog’;
run;

-FAIR ;USE-
“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.

 

How to document the testing done on the edit checks?

Since the introduction of the Electronic Data Capture (EDC) in clinical trials where data is entered directly into the electronic system, it is estimated that the errors (e.g. transcription error) have been reduced by 70% [ Clinical Data Interchange Standards Consortium – Electronic Source Data Interchange 2005].

The Data Management Plan (DMP) defines the validation test to be performed to ensure data entered into the clinical database is complete, correct, allowable, valid and consistent.

Within the DMP, we find the Data Validation Plan. Some companies call it ‘DVS’ others ‘DVP’.  The Good practices for computerized systems in regulated GxP environments defines validation as a system that assures the formal assessment and reporting of quality and performance measures for all the life-cycle stages of software and system development, its implementation, qualification and acceptance, operation, modification, qualification, maintenance, and retirement.

As an {EDC} Developer or Clinical Programmer, you will be asked to:

  • Develop test scripts and execution logs for User Acceptance Testing (UAT).
  • Coordinate of UAT of eCRF build with clinical ops team members and data management and validating documents, included but not limited to: edit check document, issue logs, UAT summary report and preparation and testing of test cases.

Remember not every EDC system is alike. Some systems allow you to perform testing on the edit checks programmed; others allow you to enter test data on a separate instance than production (PROD).

Data Validation and UAT Module.png

For example, some EDC systems facilitate re-usability:

  1. There is a built-in test section for each study – where data can be entered and are stored completely separate from production data. This allows you to keep the test data for as long as needed to serve as proof of testing.
  2. The copy function allows for a library of existing checks (together with their associated CRF pages) to be copied into a new study. If there are no changes to the standard checks or pages then reference can be made back to the original set of test data in a standards study, thus reducing the study level overhead.
  3. The fact that many of the required checks (missing data, range checks, partial dates etc.) do not require the programming of an edit check at all. Each of these and many others are already there as part of the question definition itself and therefore do not need any additional testing or documentation for each study.

If you have not documented, you have not done it-FDA

The “ideal world” scenario would be to reduce the actual edit check testing by the system generating a more “human readable” format of the edit checks. The testers that way would not have to test each boundary conditions of the edit checks once the system is validated. All they would have to do is inspect the “human readable” edit checks vs the alerts and would also be easy for the clients to read and sign off.

You can leverage the EDC systems audit trail under certain conditions. First of all – the system you are testing with must be validated in itself. Some EDC products are only ‘validated’ once a study is built on top of them – they are effectively further developed as part of a study implementation process – in this situation, I would doubt you could safely use the audit trail.

Secondly, you need to come up with a mechanism whereby you can assure that each edit check has been specifically tested – traceability.

Finally, you need to secure the test evidence. The test data inside the EDC tool must be retained for as long as the archive as part of the evidence of testing.

The worst methods in my view are paper / screenshot based. They take too long, and are largely non-reusable. My past experience has been creating test cases using MS Word then performing each step as per test case and take a screenshot, where indicated. Then attached to the final documentation and validation summary. This obviously a manual and tedious process. Some companies create test cases using HPQC or similar tool. This is a bit more automated and traceable yet, it is still prone for errors. It is better than documenting using MS Word or Excel but it is still a manual process.

Re-usability is what it is all about, but, you need to ensure you have methods for assuring the test evidence produced for edit checks you are reusing is usable as part of the re-use exercise.

Edit Check Design, Development and Testing is the largest part of any typical EDC implementation. Applying methods to maximize quality and minimize time spent is one of the areas I have spent considerable time on over the last couple of years.

For additional tips on writing effective edit checks please go here -Effective edit checks eCRFs.

To hire me for services, you may contact me via Contact Me OR Join me on LinkedIn

Source images: provided courtesy of Google images.

-FAIR USE-
“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.”

Complexity and effectiveness of edit checks

ABSTRACT
Much effort goes into the specification, development, testing and verification of programmatic edit checks to ensure that the error rate in clinical trial data is sufficiently low as to have no statistically significant effect on the overall trial results. An analysis of several thousand clinical trials, containing over 1.1 billion data values and 1.1 million edit checks, shows that the majority of edit checks (60%) have no impact on data quality; none of these 678,000 edit checks have generated a single data query or discrepancy. What can be learnt from this analysis; can we reduce the overall number of edit checks without compromising data quality; can we identify the ‘high-performing’ edit checks and improve CRF design to avoid data entry errors; are there novel methods that might achieve similar standards of data quality with less effort?

Edit checks are necessary to ensure data quality reaches acceptably high levels.

Since programming edit checks takes time and resources, it’s important to ensure that the effort invested maximizes the benefit and re-usability of each edit check.

See attached document for full article information published by:

Optimizing Data Validation by Andrew Newbigging, Medidata Solutions Worldwide, London, United Kingdom

 

Comments? Join us at {EDC Developer}

Anayansi Gamboa, MPM, an EDC Developer Consultant and clinical programmer for the Pharmaceutical and Biotech industry with more than 13 years of experience.

Available for short-term contracts or ad-hoc requests. See my specialties section (Oracle, SQL Server, EDC Inform, EDC Rave, OpenClinica, SAS and other CDM tools)

As the 3 C’s of life states: Choices, Chances and Changes- you must make a choice to take a chance or your life will never change. I continually seek to implement means of improving processes to reduce cycle time and decrease work effort.

Subscribe to my blog’s RSS feed and email newsletter to get immediate updates on latest news, articles, and tips. I am available on LinkedIn. Connect with me there for technical discussions.

Fair Use Notice: This article/video 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. If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. Fair Use notwithstanding we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their website or wants us to add their photo.

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.

Disclaimer:De inhoud van deze columns weerspiegelen niet per definitie de mening van {EDC Developer}.

Disclaimer: The legal entity on this blog is registered as Doing Business As (DBA) – Trade Name – Fictitious Name – Assumed Name as “GAMBOA”.

 

CDISC/CDASH Standards at your Fingertips

A standard database structure using CDISC (Clinical Data Interchange Standards Consortium) and CDASH (Clinical Data Acquisition Standards Harmonization) standards can facilitate the collection, exchange, reporting, and submission of clinical data to the FDA and EMEA. CDISC and CDASH standards provide reusability and scalability to EDC (electronic data capture) trials.

There are some defiance in implementing CDISC in EDC CDMS:

1. Key personnel in companies must be committed to implementing the CDISC/CDASH standards.

2. There is an initial cost for deployment of new technology: SDTM Data Translation Software, Data Storage and Hosting, Data Distribution and Reporting Software.

3. It can be difficult to understand and interpret complex SDTM Metadata concepts and the different implementation guides.

4. Deciding at what point in a study to apply the standards can be challenging: in the study design process, during data collection within the CDMS [CDASH via EDC tools], in SAS prior to report generation [ADaM], or after study completion prior to submission [SDTM].

5. Data management staff [CDM, clinical programmers], biostatisticians, and clinical monitors may find it difficult to converge on a new standard when designing standard libraries and processes.

6. Implementing new standards involves reorganizing the operations of (an organization) so as to improve efficiency [processes and SOPs].

7. Members of Data Management team must be retrained on the use of new software and CDISC/CDASH standards.

standards8. There are technical obstacles related to implementation in several EDC systems, including 8 character limitations [SAS] on numerous variables, determining when to use supplemental qualifiers versus creating new domains, and creating vertical data structure.

Comments? Join us at {EDC Developer}

Anayansi Gamboa, MPM, an EDC Developer Consultant and clinical programmer for the Pharmaceutical and Biotech industry with more than 13 years of experience.

Available for short-term contracts or ad-hoc requests. See my specialties section (Oracle, SQL Server, EDC Inform, EDC Rave, OpenClinica, SAS and other CDM tools)

As the 3 C’s of life states: Choices, Chances and Changes- you must make a choice to take a chance or your life will never change. I continually seek to implement means of improving processes to reduce cycle time and decrease work effort.

Subscribe to my blog’s RSS feed and email newsletter to get immediate updates on latest news, articles, and tips. I am available on LinkedIn. Connect with me there for technical discussions.

Fair Use Notice: This article/video 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. If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. Fair Use notwithstanding we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their website or wants us to add their photo.

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.

Disclaimer:De inhoud van deze columns weerspiegelen niet per definitie de mening van {EDC Developer}.

Disclaimer: The legal entity on this blog is registered as Doing Business As (DBA) – Trade Name – Fictitious Name – Assumed Name as “GAMBOA”.

How to write query texts – 6 template sentences

How to write queries unambiguously expressing what is asked for? Using short, polite sentences? Objectively explaining the underlying inconsistency?

First of all my general guidelines.

My preference is to use no more capitals then needed. Capitals in the middle of a query text, e.g. for CRF fields or for tick box options, could distract from getting the actual question asked. E.g. compare the same query texts, with and without extra capitals. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify Stop date. (Ensure that Stop date is after or at Start date AND that Stop date is not a future date.)

Referring to CRF fields as they are shown on the CRF. To easily find the involved field(s).

I prefer to leave any ‘the’ before a CRF field referral out of the query text. For more to-the-point query texts. E.g. compare the same query texts, with and without ‘the’ before data fields. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify the stop date. (Ensure that the stop date is after or at the start date and that the stop date is not a future date.)

Consistency in phrasing a query text can help to quickly write query texts or pre-program query texts in a structured, familiar way. That’s the thought behind the following 6 template sentences for query texts. Which you can use to help you write or program your queries.

The six ‘template’ sentences for query texts:

Please provide…

For asking the study site people to provide required data from patient care recordings. Examples: Please provide date of visit. Please provide date of blood specimen collection. Please provide platelet count. Please provide % plasma cells bone marrow aspirate. Please provide calcium result.

Please complete… For asking the study site people to complete required data as required by the study CRF design. (Not necessarily required for patient care). Examples: Please complete centre number. Please complete subject number. Other frequency is specified, please complete frequency drop-down list accordingly.

Please verify…

For asking the study site people to check date and time fields fulfilling expected timelines. Or for asking the study site people to check field formats. Examples: Please verify start date. (Ensure that start date is before date of visit.) Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify date of blood specimen collection. (Ensure that date of blood specimen collection is before or equal to date of visit and after date of previous visit.) Please verify date last pregnancy test performed. Please verify date of informed consent. (Ensure date of informed consent is equal to date of screening or prior to date of screening.) Please verify date as DDMMYYY.

…., please correct.

For asking the study site people to correct a data recording inconsistent with another data recording. Example: Visit number should be greater than 2, please correct.

…., please tick…

For asking the study site people to complete required tick boxes. Examples: Gender, please tick male or female. Pregnancy test result, please tick negative or positive. Any new adverse events or changes in adverse events since the previous visit, please tick yes or no. Laboratory assessment performed since the previous visit, please tick yes or no. LDH, please tick normal, abnormal or not done.

Please specify…

For asking the study site people to specify the previous data recording. Examples: Please specify other dose. Please specify other frequency. Please specify other method used. Please specify other indication for treatment.

Finally, for query texts popping up during CRF data recording, it could be helpful to put location information in it. Like: Page 12: Please verify start date. (Ensure that start date is after or at start date on page 11.)

Good luck finding your way to structure query texts…

Source:

This article is written by Maritza Witteveen of ProCDM. For clinical data management. You can subscribe to her blog posts at www.procdm.nl.”

Comments? Join us at {EDC Developer}

Anayansi Gamboa, MPM, an EDC Developer Consultant and clinical programmer for the Pharmaceutical and Biotech industry with more than 13 years of experience.

Available for short-term contracts or ad-hoc requests. See my specialties section (Oracle, SQL Server, EDC Inform, EDC Rave, OpenClinica, SAS and other CDM tools)

As the 3 C’s of life states: Choices, Chances and Changes- you must make a choice to take a chance or your life will never change. I continually seek to implement means of improving processes to reduce cycle time and decrease work effort.

Subscribe to my blog’s RSS feed and email newsletter to get immediate updates on latest news, articles, and tips. I am available on LinkedIn. Connect with me there for technical discussions.

Fair Use Notice: This article/video 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. If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. Fair Use notwithstanding we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their website or wants us to add their photo.

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.

Disclaimer:De inhoud van deze columns weerspiegelen niet per definitie de mening van {EDC Developer}.

Central Designer: Constants and Their Role in Query Messages

What are Constants?

Constants are that item whose value remains same during the execution of the program from the beginning till the end of a program

Pros:

  • Reusable
  • Integer range checks
  • The value of an item is constant for every subject in a study (i.e lower and upper pulse or height or temperature ranges)
  • It can be referenced in a query message when performing an action
  • Useful when accessing by several forms/checks
constant
constant ranges (min and max value)

Let’s visualize this using a programming language. Define your constant(s). Do some calculation and output the results. Your constant never changes. Only what variables, what the user input into the screen, changes.

...
/* Define a constant to convert from pounds to grams */
5:  #define GRAMS_PER_POUND 454
...
/* Declare the needed variables */
11: long weight_in_grams, weight_in_pounds;
...
/* Perform conversions */
24:
25:    weight_in_grams = weight_in_pounds * GRAMS_PER_POUND;
...
28:   
/* Display results on the screen */
...
Enter your weight in pounds: 182
Your weight in grams = 82628

A couple of points to remember when using constants:

  • drag the constant into the parameters and then drag the parameter into the message box
  • change the prefix from ‘this.’ to ‘constants.’. This will prevent from getting an error message when you check the syntax (i.e. syntax error)

Conclusion: When the parameters change, you only need to change it at the Constant (tab) study object level and avoid hard-coding each query text message. The actual value of the constant needs to be entered only once, when it is first defined.

Reference: Central Designer Best Practices

Your comments and questions are valued and encouraged.
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.