Motto of the Month – The Power of Manifestation

It does not matter how many times you ask for something. If your vibration, your energy (that includes your thoughts, your memories, etc) are not vibrating at the exact same rate as your desire, IT WILL NEVER HAPPEN.

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7 (Audio)Books You Should Read in 2018

Do you want to be successful? Looking for your life purpose? Are you feeling down? Unmotivated? Lost? Do you want a better career or business? The following books have helped me obtain the life I desire and guide me through difficult times.

My goals and desires are different than yours but I am sure you will benefit from these books. If anything, you will feel motivated again and eager to embark a new life. Good luck and share your success stories.

  1. The Richest Man In Babylon –  Napoleon Hill

2.  The Strangest Secret by Earl Nightingale

3. Think And Grow Rich  –  Napoleon Hill

4. Change your life in 20 minutes – Earl Nightingale

5.  Use This For 30 days and Watch Your Prosperity Grow! – Catherine Ponder

6.  W. Clement Stone and Napoleon Hill – Success Through A Positive Mental Attitude

7. Ask and it is Given – Abraham Hicks

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Cracking the Code: Estimate at Completion (EAC) of a Clinical Trial

Project management is a continuous loop of planning what to do, checking on progress, comparing progress to plan, taking corrective action if needed, and re-planning. The fundamental items to plan, monitor, and control are time, cost, and performance so that the project stays on schedule, does not exceed its budget, and meets its specifications.  Of course all of these activities are based on having an agreed upon Work Breakdown Structure (tasks/activities) on which to base the schedule and cost estimates.  During the planning phase of a project, the project manager with the assistance of the project team needs to define the process and procedures that will be used during the implementation phase to monitor and control the project’s performance.

Productivity in the pharmaceutical/biotech/medical device industry is going down. Some compounds have reached the billions expenditures cost without any guarantee that it will ever be approved or reach the market.  So how can we evaluate the performance of some of these clinical trials?

I will not go into details in the degree of project management activities managed and performed by a data manager since this can vary widely per company.  A good clinical data manager or manager of data management should be able to implement basic PM principles that will improve quality and timeliness of a clinical trial, regardless if the trial is fully outsourced (e.g. CRO performed most of the work).

You can find my article about the Role of Project Management in Clinical Data Management (2012) here for further reading.

So what is Estimate at Completion or EAC? or What is the project likely to cost?

There are several methods we could use to calculate EAC.

Let’s look at one formula. EAC =  AC (Actual Cost) + ETC (Estimate to Complete)  so what happens when you don’t know the ETC?

We could use the following formula to derive that value: ETC = (BAC – EV) / CPI =>>>>??? So what? More formulas? How do I get BAC or EV or CPI?

Let’s look at those in more details.

 BAC =>>>Budget at Completion (how much did you
budget for the total project?)
CPI =>>> Cost Performance Index (CPI): BCWP/ACWP

EV = Earned Value

Earned Value Analysis example for a phase 1 trial (*figures in the thousands / millions = fictitious  numbers)

The final clinical trial results includes 100 subjects. The estimated cost is $20 per subject.  That results in an estimated budget of $2000 (100 x 20). During the planning, the CRO indicated that would be able to enroll 5 subjects per week.  Therefore the estimated duration of the trial is 20 weeks (100 / 5)

EV blocks: From the project plan

Estimated Budget: $2000

Estimated Schedule: 20 weeks

Planned Value (PV): at the end of the trial is $2000

Variance between planned and actual at the end of the first week:

Based on the estimated scheduled, I should have 25 subjects enrolled. At $20 per subject, the planned value at the end of the week is $500 (25 x 20)

PV = $500

At the end of the first week, the CRO reports that he has enrolled 20 subjects  and the actual cost of that study is $450. With this information we can look at schedule and cost variance.

SV = EV – PV

SV = $400 – $500 = – 100 ($100 work of subject recruitment is behind schedule).

CV = EV – AC

CV = $400 – $450 = -50 ($50 work of the project is over budget)

*negative figures means bad.

Using early results to predict later results:

Schedule Performance Index (SPI)

SPI = EV/PV

SPI = 400/500 = .80

Cost Performance Index (CPI)

CPI = EV/AC

CPI = 400/450 = .89 –> over budget or expending more

These rations can be used to estimate performance of the project to completion based on the early actual experience.

Estimate to Completion (ETC)
ETC= (PV at completion) – EV)/CPI

ETC= (2000 – 400)/CPI

ETC = (1600/.89) =$ 1798 from end of week one (after 5 days) and it will take additional $1798 to complete the study

Estimate at Completion (EAC)

EAC = AC + ETC

EAC = 450 + 1798 = $2248

If nothing changes, based on the actual results at the end of the first week, the study is estimated  to cost $2248 (rather than the planned cost of $2000) and will take 20 percent longer.

The formulas assumes that the accumulative performance reflected in the CPI is likely to continue for the duration of the project.

You do not need to memorize all of these formulas. There are plenty of tools in the industry that does the computation for you. But if you do not have it available, you can use Excel, set-up your template and plug in the numbers.

Earned Value

 

 

 

 

 

 

 

As per PMI – PMBOK definition, Cost management “…includes the processes involved in estimating, budgeting, and controlling costs so that the project can be completed within the approved budget.”   A Guide to the Project Management Body of Knowledge (PMBOK® Guide).

We have shown you, that PM tools such as Earned Value  Analysis, can be applied to clinical trials or specific work break down (WBS) activities within the data management team.

Based on the above outcome of the project performance related to the schedule, the data manager should be able to determine if she should modify the current plan or revise the original plan.

It is a perfect tool for data managers and managers of data managers and could be part of your risk based processes.

If bringing efficiency, improving data quality and significantly reducing programming time after implementing CDISC standards is on your radar screen, I’d love to chat when it’s convenient. All the best.

Anayansi Van Der Berg 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. SAS, CDASH/SDTM (CDISC standards implementation and mapping), SAS QC checks and clinical data reporting.

Source:

A Guide to the Project Management Body of Knowledge (PMBOK® Guide).

Notes from my PM class at Keller 2007-2009

Images – Google images

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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

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How to Avoid Electronic Data Integrity Issues: 7 Techniques for your Next Validation Project

The idea of this article was taking (with permission from the original authors) from Montrium:  how-to-avoid-electronic-data-integrity-issues-7-techniques-for-your-next-validation-project

Regulatory agencies around the globe are causing life science companies to be increasingly concerned with data integrity.  This comes with no surprise given that Guidance Documents for Data Integrity have been published by the MHRAFDA (draft), and WHO (draft).  In fact, the recent rise in awareness of the topic has been so tremendous that, less than two years after the original publication, the MHRA released a new draft of its guidance whose scope has been broadened from GMP to all GxP data.

Is data integrity an issue of good documentation practices? You can read GCP information about this topic here.

Good Documentation Practices for SAS / EDC Developers

Are you practising GCP?

In computerised systems, failures in data integrity management can arise from poor or complete lack of system controls.  Human error or lack of awareness may also cause data integrity issues.  Deficiencies in data integrity management are crucial because they may lead to issues with product quality and/or patient safety and, ultimately may manifest themselves through patient injury or even death.

I recently was at the vendor qualification tool that uses a hand held device to read data while the physician or expert manually put pressure on someone’s body parts (e..g. pain related). I was not impressed. Even though it seems like a nice device with its own software, the entire process was manual and therefore, questionable data integrity. The measurement seems to be all over the place and you would need the right personnel at the clinical site to perform a more accurate reading since again, it was all manual and dependent of someone else used of the device.

I also questioned the calibration of this device. The sale’s person answer ? “Well, it is reading 0 and therefore, it is calibrated.”….Really? You mean to tell me you have no way of proving when you perform calibration? Where is the paper trail proving your device is accurate? You mean to tell me I have to truth your words? Or your device’s screen that reads ‘0’? Well, I have news for you. Tell that to the regulators when they audit the trial.

What is Data Integrity?

Data can be defined as any original and true copy of paper or electronic records.  In the broadest sense, data integrity refers to the extent to which data are complete, consistent and accurate.

To have integrity and to meet regulatory expectations, data must at least meet the ALCOA criteria. Data that is ALCOA-plus is even better.

Alcoa

 

What is a Computerised System?

computerised system is not only the set of hardware and software, but also includes the people and documentation (including user guides and operating procedures) that are used to accomplish a set of specific functions.  It is a regulatory expectation that computer hardware and software are qualified, while the complete computerised system is validated to demonstrate that it is fit for its intended use.

How can you demonstrate Electronic Data Integrity through Validation?

Here are some techniques to assist you in ensuring the reliability of GxP data generated and maintained in computerised systems.

Specifications

What to do

Why you should do this

Outline your expectations for data integrity within a requirements specification.

For example:

  • Define requirements for the data review processes.
  • Define requirements for data retention (retention period and data format).
Validation is meant to demonstrate a system’s fitness for intended use.  If you define requirements for data integrity, you will be more inclined to verify that both system and procedural controls for data integrity are in place.
Verify that the system has adequate technical controls to prevent unauthorised changes to the configuration settings.

For example:

  • Define the system configuration parameter within a configuration specification.
  • Verify that the system configuration is “locked” to end-users.  Only authorized administrators should have access to the areas of the system where configuration changes can be made.
The inspection agencies expect you to be able to reconstruct any of the activities resulting in the generation of a given raw data set.  A static system configuration is key to being able to do this.

 

Verification of Procedural Controls

What to do

Why you should do this

Confirm that procedures are in place to oversee the creation of user accounts.

For example:

  • Confirm that user accounts are uniquely tied to specific individuals.
  • Confirm that generic system administrator accounts have been disabled.
  • Confirm that user accounts can be disabled.
Shared logins or generic user accounts should not be used since these would render data non-attributable to individuals.

System administrator privileges (allowing activities such as data deletion or system configuration changes) should be assigned to unique named accounts.  Individuals with administrator access should log in under his named account that allows audit trails to be attributed to that specific individual.

Confirm that procedures are in place to oversee user access management.

For example:

  • Verify that a security matrix is maintained, listing the individuals authorized to access the system and with what privileges.
A security matrix is a visual tool for reviewing and evaluating whether appropriate permissions are assigned to an individual. The risk of tampering with data is reduced if users are restricted to areas of the system that solely allow them to perform their job functions.
Confirm that procedures are in place to oversee training.

For example:

  • Ensure that only qualified users are granted access to the system.
People make up the part of the system that is most prone to error (intentional or not).  Untrained or unqualified users may use the system incorrectly, leading to the generation of inaccurate data or even rendering the system inoperable.

Procedures can be implemented to instruct people on the correct usage of the system.  If followed, procedures can minimize data integrity issues caused by human error. Individuals should also be sensitized to the consequences and potential harm that could arise from data integrity issues resulting from system misuse.

Logical security procedures may outline controls (such as password policies) and codes of conduct (such as prohibition of password sharing) that contribute to maintaining data integrity.

 

Testing of Technical Controls

What to do

Why you should do this

Verify calculations performed on GxP data.

For example:

  • Devise a test scenario where input data is manipulated and double-check that the calculated output is exact.
When calculations are part of the system’s intended use, they must be verified to ensure that they produce accurate results.
Verify the system is capable of generating audit trails for GxP records.

For example:

  • Devise a test scenario where data is created, modified, and deleted.  Verify each action is captured in a computer-generated audit trail.
  • Verify the audit trail includes the identity of the user performing the action on the record
  • Verify the audit trail includes a time stamp
  • Verify the system time zone settings and synchronisation.
With the intent of minimizing the falsification of data, GxP record-keeping practices prevent data from being lost or obscured.  Audit trails capture who, when and why a record was created, modified or deleted.  The record’s chronology allows for reconstruction of the course of events related to the record.

The content of the audit trails ensures that data is always attributable and contemporaneous.

For data and the corresponding audit trails to be contemporaneous, system time settings must be accurate.

 

 

 

Who can delete data?

Adequately validated and have sufficient controls to
prevent unauthorized access or changes to data.

Implement a data integrity lifecycle concept:

  • Activate audit trail and its backup
  • Backup and archiving processes
  • Disaster recovery plan
  • Verification of restoration of raw data
  • Security, user access and role privileges (Admin)

Warning Signs – Red Flags

  • Design and configuration of systems are poor
  • Data review limited to printed records – no review
    of e-source data
  • System administrators during QC, can delete data (no proper documentation)
  • Shared Identity/Passwords
  • Lack of culture of quality
  • Poor documentation practices
  • Old computerized systems not complying with part 11 or Annex 11
  • Lack of audit trail and data reviews
  • Is QA oversight lacking? Symptom of weak QMS?
I love being audited

 

 

 

 

 

 

Perform Self Audits

  • Focus on raw data handling & data review/verification
  • Consider external support to avoid bias
  • Verify the expected sequence of activities: dates,
    times, quantities, identifiers (such as batch,
    sample or equipment numbers) and signatures
  • Constantly double check and cross reference
  • Verify signatures against a master signature list
  • Check source of materials received
  • Review batch record for inconsistencies
  • Interview staff not the managers

FDA 483 observations

“…over-writing electronic raw data…..”

“…OOS not investigated as required by SOP….”

“….records are not completed contemporaneously”

“… back-dating….”

“… fabricating data…”

“…. No saving electronic or hard copy data…”

“…results failing specifications are retested until
acceptable results are obtained….”

  • No traceability of reported data to source documents

Conclusion:

Even though we try to comply with regulations (regulatory expectations from different agencies e.g. EMA, MHRA, FDA, etc), data integrity is not always easy to detect. It is important the staff working in a regulated environment be properly trained and continuous refresher provided through their career (awareness training of new regulations and updates to regulations).

Companies should also integrate a self-audit program and develop a strong quality culture by implementing lesson learned from audits.

Sources:

You can read more about data integrity findings by searching the followng topics:

MHRA GMP Data Integrity Definitions & Guidance for the Industry,
MHRA DI blogs: org behaviour, ALCOA principles
FDA Warning Letters and Import Alerts
EUDRA GMDP database noncompliance

The Mind-Numbing Way FDA Uncovers Data
Integrity Laps”, Gold Sheet, 30 January 2015

Data Integrity Pitfalls – Expectations and Experiences

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Good Clinical Practice – The Bible

Good Clinical Practice (GCP) is an international ethical and scientific quality standard for
designing, conducting, recording and reporting trials that involve the participation of
human subjects. Compliance with this standard provides public assurance that the rights,
safety and well-being of trial subjects are protected, consistent with the principles that have
their origin in the Declaration of Helsinki, and that the clinical trial data are credible.

Below is the link to the most common terms used in clinical trials (for reference). Use it as your leisure, during work hours and day-to-day work as a clinical researcher.

Good Clinical Practice Bible – Terminologies

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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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

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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.

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.

 

 

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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, 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.

 

Using PROC UNIVARIATE to Validate Clinical Data

Using PROC UNIVARIATE to Validate Clinical Data

When your data isn’t clean, you need to locate the errors and validate them.  We can use SAS Procedures to determine whether or not the data is clean. Today, we will cover the PROC  UNIVARIATE procedure.

  • First step is to identify the errors in a raw data file. Usually, in our DMP, in the DVP/DVS section, we can identify what it is considered ‘clean’ or data errors.
    • Study your data
  • Then validate using PROC UNIVARIATE procedure.
  • Find extreme values

When you validate your data, you are looking for:

  • Missing values
  • Invalid values
  • Out-of-ranges values
  • Duplicate values

Previously, we used PROC FREQ to find missing/unique values. Today, we will use PROC UNIVARIATE which is useful for finding data outliers, which are data that falls outside expected values.

proc univariate data=labdata nextrobs=10;
var LBRESULT;
run;

Lab data result using Univariate

 

 

 

 

 

 

 

 

 

For validating data, you will be more interested in the last two tables from this report. The missing values table shows that the variable LBRESULT has 260 missing values. There are 457 observations. The extreme observations table can tell us the lowest and highest values (possible outliers) from our dataset. The nextrobs=10 specify the number of extreme observations to display on the report. To suppress it use nextrobs=0.

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

 

Using PROC FREQ to Validate Clinical Data

Using PROC FREQ to Validate Clinical Data

When your data isn’t clean, you need to locate the errors and validate them.  We can use SAS Procedures to determine whether or not the data is clean. Today, we will cover the PROC FREQ procedure.

  • First step is to identify the errors in a raw data file. Usually, in our DMP, in the DVP/DVS section, we can identify what it is considered ‘clean’ or data errors.
    • Study your data
  • Then validate using PROC FREQ procedure.
  • Spot distinct values

When you validate your data, you are looking for:

  • Missing values
  • Invalid values
  • Out-of-ranges values
  • Duplicate values

Previously, we used PROC PRINT to find missing/invalid values. Today, we will use PROC FREQ  to view a frequency table of the unique values for a variable. The TABLES statement in a PROC FREQ step specified which frequency tables to produce.

proc freq data=labdataranges nlevels;
table _all_ / noprint;
run;

So how many unique lab test do we have on our raw data file? We know that our sas data set has 12 records. The Levels column from this report,  the labtest=3 uniques. Which means, we must have 9 duplicates labtest in total. For this type of data [lab ranges] though, this is correct. We are using it as an example as you can check any type of data.

Proc Freq sas

 

 

 

Lab test data ranges

 

 

 

 

 

 

 

 

 

 

 

 

So remember, to view the distinct values for a variable, you use PROC FREQ that produces frequency tables (nway/one way) . You can view the frequency, percent, cumulative frequency, and cumulative percentage. With the NLEVELS options, PROC FREQ displays a table that provides the number of distinct values for each variable name in the table statement.

Example: SEX variable has the correct values F or M as expected; however, it is missing for two observations.

Missing values proc freq

 

 

 

 

 

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

Using PROC PRINT to Validate Clinical Data

Using PROC PRINT to Validate Clinical Data

When your data isn’t clean, you need to locate the errors and validate them.  We can use SAS Procedures to determine whether or not the data is clean. Today, we will cover the PROC PRINT procedure.

  • First step is to identify the errors in a raw data file. Usually, in our DMP, in the DVP/DVS section, we can identify what it is considered ‘clean’ or data errors.
    • Study your data
  • Then validate using PROC PRINT procedure.
  • We will clean the data using data set steps with assignments and IF-THEN-ELSE statements.

When you validate your data, you are looking for:

  • Missing values
  • Invalid values
  • Out-of-ranges values
  • Duplicate values

In the example below, our lab data ranges table we find missing values. We also would like to update the lab test to UPPER case.

Clinical Raw data
Proc Print data val code
PROC PRINT output – data validation

 

From the screenshot above, our PROC PRINT program identified all missing / invalid values as per our specifications. We need to clean up 6 observations.

Cleaning Data Using Assignment Statements and If-Then-Else in SAS

We can use the data step to update the datasets/tables/domains when there is an invalid or missing data as per protocol requirements.

In our example, we have a lab data ranges for a study that has started but certain information is missing or invalid.

To convert our lab test in upper case, we will use an assignment statement. For the rest of the data cleaning, we will use IF statements.

Proc Print data cleaning

 

 

 

 

 

 

 

Data Validation and data cleaning final dataset

 

 

 

 

 

 

 

From our final dataset, we can verify that there are no missing values. We converted our labTest in uppercase and we updated the unit and  EffectiveEnddate to k/cumm and 31DEC2025 respectively.

You cannot use PROC PRINT to detect values that are not unique. We will do that in our next blog ‘Using PROC FREQ to Validate Clinical Data’. To find duplicates/remove duplicates, check out my previous post-Finding Duplicate data.

or use a proc sort data=<dataset> out=sorted nodupkey equals; by ID; run;

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

 

Case Study 4: A Full Data Management Solution

Working in a Collaborative Environment

The Scenario:

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

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

Ra eClinica Results:

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

Anayansi Gamboa- Virtual DM Service from RA eClinica

RA eClinica is a established consultancy company for all essential aspects of statistics, clinical data management and EDC solutions. Our services are targeted to clients in the pharmaceutical and biotech sector, health insurers and medical devices.

The company is headquarter in Panama City and representation offices with business partners in the United States, India and the European Union. For discussion about our services and how you can benefit from our SMEs and cost-effective implementation CDISC SDTM clinical data click here.

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

Case Study 3: Out-of-Box Solution

The Scenario:

The Sponsor required a solution to effectively manage and control users (internal and external).
RA eClinica Solution:

    • RA eClinica collaborated with the Sponsor to develop and implement a user management system that involved training tracking record (LMS), user access request, Role-based access control
    • Develop, deploy and host the clinical documentation service and provide customer support.

Ra eClinica Results:

    • Development of an electronic tool to manage the program and provide ongoing operational management support..
    • Reports are made accessible based on permission on a web browser CFR-Part 11 compliance is maintained on security and privacy of data.
    • Reports are XML-tagged for further integration with in-house systems and third party service providers,
    • Integrated Help desk support system

Anayansi gamboa - Out of the box solutions by RA eClinica

RA eClinica is a established consultancy company for all essential aspects of statistics, clinical data management and EDC solutions. Our services are targeted to clients in the pharmaceutical and biotech sector, health insurers and medical devices.

For discussion about our services and how you can benefit from our SMEs and cost-effective implementation CDISC SDTM clinical data click here.

Case Study 1: Stand Alone Satellite Office Solution

Integrated Into Sponsor’s Clinical Data Management (CDM) Environment

Anayansi gamboa - offshore Panama Data management

 

 

 

 

The Scenario:

The Sponsor was in need of a data management team to function in an integrated manner as an extension of the Sponsor’s CDM team. Based on geographic and offices constraints, coupled with the large volume of work, hiring individual contract resources on-site was not an option.

RA eClinica Solution:

  • RA eClinica Data Management Operations collaborated with Sponsor to develop CDM metrics, collaboration model and workflow, enabling the team to work across 3+ protocols
  • RA eClinica provided a full solution of 5+ CDM resources, project management, dedicated secure facilities integrating into Sponsor’s eClinical and CDMS databases.

Ra eClinica Results:

  • Develop of a long-term, efficient and cost-effective CDM solution.

RA eClinica is a established consultancy company for all essential aspects of statistics, clinical data management and EDC solutions. Our services are targeted to clients in the pharmaceutical and biotech sector, health insurers and medical devices.

The company is headquarter in Panama City and representation offices with business partners in the United States, India and the European Union. For discussion about our services and how you can benefit from our SMEs and cost-effective implementation CDISC SDTM clinical data click here.

PM Hats – Six Thinking Hats in Project Management

Six Thinking Hats

Looking at a Decision From All Points of View

‘Six Thinking Hats’ is an important and powerful technique. It is used to look at decisions from a number of important perspectives. This forces you to move outside your habitual thinking style, and helps you to get a more rounded view of a situation.

This tool was created by Edward de Bono’s book ‘6 Thinking Hats‘.

Many successful people think from a very rational, positive viewpoint. This is part of the reason that they are successful. Often, though, they may fail to look at a problem from an emotional, intuitive, creative or negative viewpoint. This can mean that they underestimate resistance to plans, fail to make creative leaps and do not make essential contingency plans.

Similarly, pessimists may be excessively defensive, and more emotional people may fail to look at decisions calmly and rationally.

If you look at a problem with the ‘Six Thinking Hats’ technique, then you will solve it using all approaches. Your decisions and plans will mix ambition, skill in execution, public sensitivity, creativity and good contingency planning.

How to Use the Tool

You can use Six Thinking Hats in meetings or on your own. In meetings it has the benefit of blocking the confrontations that happen when people with different thinking styles discuss the same problem.

Each ‘Thinking Hat’ is a different style of thinking. These are explained below:

  • White Hat: neutral and objective, concerned with facts and figures
    With this thinking hat you focus on the data available. Look at the information you have, and see what you can learn from it. Look for gaps in your knowledge, and either try to fill them or take account of them.This is where you analyze past trends, and try to extrapolate from historical data.
  • Red Hat: the emotional view
    ‘Wearing’ the red hat, you look at problems using intuition, gut reaction, and emotion. Also try to think how other people will react emotionally. Try to understand the responses of people who do not fully know your reasoning.
  • Black Hat: careful and cautious, the “devil’s advocate” hat * 
    Using black hat thinking, look at all the bad points of the decision. Look at it cautiously and defensively. Try to see why it might not work. This is important because it highlights the weak points in a plan. It allows you to eliminate them, alter them, or prepare contingency plans to counter them.Black Hat thinking helps to make your plans ‘tougher’ and more resilient. It can also help you to spot fatal flaws and risks before you embark on a course of action. Black Hat thinking is one of the real benefits of this technique, as many successful people get so used to thinking positively that often they cannot see problems in advance. This leaves them under-prepared for difficulties.
  • Yellow Hat: sunny and positive 
    The yellow hat helps you to think positively. It is the optimistic viewpoint that helps you to see all the benefits of the decision and the value in it. Yellow Hat thinking helps you to keep going when everything looks gloomy and difficult.
  • Green Hat: associated with fertile growth, creativity, and new ideas
    The Green Hat stands for creativity. This is where you can develop creative solutions to a problem. It is a freewheeling way of thinking, in which there is little criticism of ideas. A whole range of creativity tools can help you here.
  • Blue Hat: cool, the color of the sky, above everything else-the organizing hat 
    The Blue Hat stands for process control. This is the hat worn by people chairing meetings. When running into difficulties because ideas are running dry, they may direct activity into Green Hat thinking. When contingency plans are needed, they will ask for Black Hat thinking, etc.

Exercise:

Here’s an exercise (inspired by Bono ideas) which will work very well with those who have been required to read Six Thinking Hats prior to getting together to brainstorm. Buy several of those delightful Dr. Seuss hats (at least one of each of the six different colors, more if needed) and keep the hats out of sight until everyone is seated. Review the agenda. Review what de Bono says about what each color represents. Then distribute the Dr. Seuss hats, making certain that someone is wearing a hat of each color. Proceed with the discussion, chaired by a person wearing a Blue or White hat. It is imperative that whoever wears a Black hat, for example, be consistently negative and argumentative whereas whoever wears a Yellow must be consistently positive and supportive. After about 15-20 minutes, have each person change to a different colored hat. Resume discussion.

Six Thinking Hats” is about improving communication and decision-making in groups.

Summary: Bono puts thinking into steps: 1. Information 2. Benefits 3.Critical thinking 4. Feelings 5. Creative thinking 6. Thinking about the thinking and creating and action plan for implementation.

How would you incorporate the ‘Six Thinking Hats’ in clinical data management?

Reference:

Six Thinking Hats by Edward de Bono, 1999

http://www.mindtools.com

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Data Management Plan in Clinical Trials

 

The preparation of the data management plan (DMP) is a simple, straightforward approach designed to promote and ensure comprehensive project planning.

The data management plan typically contains the following items. They are:

  1. Introduction/Purpose of the document
  2. Scope of application/Definitions
  3. Abbreviations
  4. Who/what/where/when
  5. Project Schedule/Major Project Milestones
  6. Updates of the DMP
  7. Appendix

The objective of this guidelines is to define the general content of the Data Management Plan (DMP) and the procedures for developing and maintaining this document.

The abbreviation section could include all acronyms used within a particular study for further clarification.

e.g. CRF = Case Report Form
TA = Therapeutic Area

The Who/What/Where/When section should describe the objective of the study specific data management plans for ABC study. This section provides detail information about the indications, the number of subjects planned for the study, countries participating in the clinical trial, monitoring guidelines (SDV) or partial SDV, if any CROs or 3rd party are involved in the study (e.g. IVRS, central labs), which database will be used to collect study information (e.g. Clintrial, Oracle Clinical, Medidata Rave or Inform EDC).

The Appendix provides a place to put supporting information, allowing the body of the DMP to be kept concise and at more summary levels. For example, you could document Database Access of team members, Self-evident correction plan, Data Entry plan if using Double-data entry systems or Paper-Based clinical trials systems.

Remember, this is a living document and must be updated throughout the course of the clinical trial.

If problems arise during the life of a project, our first hunch would be that the project was not properly planned.

Reference: Role of Project Management in Clinical Trials
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.

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

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

Data Management: Queries in Clinical Trials

When an item or variable has an error or a query raised against it, it is said to have a “discrepancy” or “query”.

All EDC systems have a discrepancy management tool or also refer to “edit check” or “validation check” that is programmed using any known programming language (i.e. PL/SQL, C# sharp, SQL, Python, etc).

So what is a ‘query’? A query is an error generated when a validation check detects a problem with the data. Validation checks are run automatically whenever a page is saved “submitted” and can identify problems with a single variable, between two or more variables on the same eCRF page, or between variables on different pages. A variable can have multiple validation checks associated with it.

Errors can be resolved in several ways:

  • by correcting the error – entering a new value for example or when the datapoint is updated
  • by marking the variable as correct – some EDC systems required additional response or you can raise a further query if you are not satisfied with the response

Dealing with queries
Queries can be issued and/or answered by a number of people involved in the trial. Some of the common setups are: CDM, CRA or monitors, Site or coordinators.

Types of Queries

  • Auto-Queries or Systems checks
  • Manual Queries
  • Coding Queries
  • SDV related Queries generated during a Monitor visit
  • External Queries – for external loaded data in SAS format

EDC Systems and Discrepancy Output Examples

InForm

Note: All queries are associated to a single data item relevant to that query.

RAVE

Note: Users are only able to see / perform an action on a query based on their
role and the permissions via Core Config.

Timaeus

Note: Queries are highlighted by a red outline and a Warning icon.

OpenClinica

Note: Extensive interfaces for data query.

Query Metrics – It is important to measure the performance of your clinical trials.
Metrics are the same for all clinical studies but not all EDC systems are the same. Standardized metrics encourage performance improvement, effectiveness, and efficiency. Some common metrics are:

  • Outstanding Query
  • Query Answer Time
  • Average Time to Query Resolution
  • Number of closed discrepancies on all ongoing studies

Data management’s experience with data queries in clinical trials

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.”

Trademarks: InForm is a trademark or registered trademark of Oracle Corporation. Rave is a trademark or registered trademark of Medidata. Timaeus is a trademark or registered trademark of Cmed Clinical Research.


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.

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

 

The Project Manager is responsible for the development, oversight of implementation, and communication of clinical research studies.

So what is a Project?

A project is a work effort with a definite beginning and end, an identifiable end result (deliverable), and usually has limits on resources, costs and/or schedule.

What is Project Management?

The application of knowledge, skills, tools, and techniques to project tasks in order to meet project requirements.

In order to be a successful project manager, you need to understand the “Tripple Constraint” and how they affect your project. Let’s look up the WBS-edit checks:

Note: I will refer a project = clinical study

Scope: What is in the contract? How many edit checks, SAS checks and manual checks are required in this study? What is the effort per edit check, SAS check and manual check?

The goal is to convert the idea of data management to that of statistical analysis – an analyzable database.

Time: What are the deliverables and timelines? What resources are needed?

Cost: What are the budget restrictions? Are there any risks associated with any changes?

Project Planning: During the planning of a clinical study, we identify the project scope, develop the project management plan and we identify and schedule the clinical study activities.

Some questions might arise during the project planning phase: how many sites/subjects and pages will be collected?Who will attend team meetings? what study fields will be code (i.e. Adverse Event term)?

Other important activities that the project manager and clinical team members will need to be involved:

Work Break Down (WBS) – it is the list of activities that will be performed during the course of a clinical study.

Resourcing – it is important to assign the right person to a particular task based on skills, education and experience.

ICH Guidelines ‘…all personnel involved in clinical trials must be qualified and properly trained to perform their respective tasks…’

Estimating Cost – look at historical data as well as good estimates from effort per unit and units using your WBS as references.

Scheduling and Budgeting – you will be able to build schedules and budgets that transform project constraints into project success after you successfully construct your Work Breakdown Structures (WBS) and network diagrams and estimate task durations.

Projects managers used techniques for employed to establish project. Project Manager can decide which activity can be delayed without affecting the duration of the projects. They help improving quality and reduce the risks and costs related with the projects.

A recent survey by the Project Management Institute provided 10 challenges affecting project managers. This research intended to identify key factors affecting project team performance:

  1. Changes to Project Scope (Scope Creep)
  2. Resources are Inadequate (Excluding Funding)
  3. Insufficient Time to Complete the Project
  4. Critical Requirements are Unspecified or Missing
  5. Inadequate Project Testing
  6. Critical Project Tasks are Delivered Late
  7. Key Team Members Lack Adequate Authority
  8. The Project Sponsor is Unavailable to Approve Strategic Decisions
  9. Insufficient Project Funding
  10. Key Team Members Lack Critical Skills

Another question to ask is what tools are available to help you get the job done?

  1. Resource allocation (and the software’s ability to easily display staff who were overallocated)
  2. Web-based/SaaS option
  3. Cost/Price of the system (big one!)
  4. Contractual terms we could enter into (i.e. 6 months, 12 months, month to month)
  5. Ability to demo the software and for how long
  6. What sort of customizations could be made to the software after purchase
  7. Types of customers the software has served
  8. Report types
  9. Ability to sync with accounting software and which ones, if so
  10. Timeline generation capabilities and import function with MS Project
  11. Ability to create template projects
  12. Ability to alert on early warning signs (i.e. budget overruns over 10%)

It is suggestted that you review each suggestion on project management tool very, very carefully to determine how it fits your processes.

Your organization’s processes are unique to your organization; no other organization anywhere has quite the same processes. So what may work for one organization may not necessarily work for you. Your organization developed its processes to suit your particular corporate culture, the particular collective character attributes of the employees (their experience, etc.), the type of projects that you execute and the particular types customers/clients that you have (especially the regular ones).

You now have to make sure that the tools you choose work for you and your particular processes. Do not change your processes again to suit whatever workflow (process) is dictated by the fancy tool that the fancy salesman sold to you; you are likely to find that the tool-dictated workflows do not work that well in your organization, with the result that the employees will give up following processes and/or give up using the tool, throwing everything into chaos again.

Be careful if you are looking at tools that offer to do a number of different functions or can be made to do any function you want it to do. They seldom do the job that you bought it for particularly well. For example, I have worked with a tool that was advertised as a combination issue tracking and defect/bug tracking tool. It was used as a defect tracking tool but it was very poor; it was tremendously difficult to make it prepare useful reports. A hand-written tool set up in a spreadsheet (e.g. Microsoft Excel) or database (e.g. Microsoft Access) would have worked better.

That said, there are tools out there that are specific to one particular function but do offer flexible workflows – they may be modified to match whatever processes your organization already follows.

If your organization has just started to organize the PM processes and PMO that would mean processes & other related areas are not explicitly defined. So there may be a huge risk trying to adopt an integrated and centralized project management system. It is more likely to offer you a very comprehensive, complex but expensive solution wherein your problem is still not defined completely. In such a case you are just not ready with the environment and process maturity that an integrated tool requires prior to implementation.

A more efficient approach should be iterative, incremental and adaptive in nature. That means you shall use simple, not so expensive tools with limited scope to begin with; they can be tools with basic functionalities of WBS, scheduling, traceability and custom datasheets. These tools should have capability to exchange data both ways with more commonly uses tools like MS Excel, MS Project, and Word etc. The processes are likely to mature over time and we will then know the real effectiveness of these basic tools in the context of company requirements. That may be the time to analyze and switch to more integrated solutions.

One important key to remember. The role of project management in clinical trials is evolving. There is a debate about who should be the ‘project manager’ for a particular clinical study. CRA or Clinical Data Manager or an independent project manager? Let’s review their roles within data management.

Clinical Research Associate (CRA): main function is to monitor clinical trials. He or she may work directly with the sponsor company of a clinical trial, as an independent freelancer or for a Contract Research Organization (CRO). A clinical research associate ensures compliance with the clinical trial protocol, checks clinical site activities, makes on-site visits, reviews Case Report Forms (CRFs) and communicates with clinical research investigators. A clinical research associate is usually required to possess an academic degree in Life Sciences and needs to have a good knowledge of Good clinical practice and local regulations. In the United States, the rules are codified in Title 21 of the Code of Federal Regulations. In the European Union these guidelines are part of EudraLex. In India he / she requires knowledge about schedule Y amendments in drug and cosmetic act 1945.

Clinical Data Manager (CDM): plays a key role in the setup and conduct of a clinical trial. The data collected during a clinical trial will form the basis of subsequent safety and efficacy analysis which in turn drive decision-making on product development in the pharmaceutical industry. The Clinical Data Manager will be involved in early discussions about data collection options and will then oversee development of data collection tools based on the clinical trial protocol. Once subject enrollment begins the Clinical Data Manager will ensure that data is collected, validated, complete and consistent. The Clinical Data Manager will liaise with other data providers (eg a central laboratory processing blood samples collected) and ensure that such data is transmitted securely and is consistent with other data collected in the clinical trial. At the completion of the clinical trial the Clinical Data Manager will ensure that all data expected to be captured has been accounted for and that all data management activities are complete. At this stage the data will be declared final (terminology varies but common descriptions are Database Lock and Database Freeze) and the Clinical Data Manager will transfer data for statistical analysis.

Clinical Data Management (CDMS) Tools: (we will review each of them on a separate discussion)

  • Standard Operating Procedures (SOPs)
  • The Data Management Plan (DMP)
  • Case Report Form Design (CRF)
  • Database Design and Build (DDB)
  • Validation Rules also known as edit checks
  • User Acceptance Testing (UAT)
  • Data Entry (DE)
  • Data Validation (DV)
  • Data Queries (DQ)
  • Central Laboratory Data (CLD)
  • Other External Data
  • Serious Adverse Event Reconciliation (SAE)
  • Patient Recorded Data (PRO)
  • Database finalization and Extraction
  • Metrics and Tracking – see BioClinica article on Metrics
  • Quality Control (QC)- see discussion on A QC Plan for A Quality Clinical Database

In conclusion, a key component of a successful clinical study is delivering the project rapidly and cost effectively. Project managers must balance resources, budget and schedule constraints, and ever-increasing sponsor expectations.

Source:

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

 

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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“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.”

Legalese: Employment Contracts vs At Will Contracts Part I

In this world, we have two kinds of contracts, invisible contracts; those that somehow we have with governments (there are no paper trails and because they say so) and the paper contracts, those in which we have at least two parties agreeing to terms and conditions written on a piece of paper.

For the purpose of this article, we will refer to Employer or Company and Employee or consultants or freelancer moving forward.

the typed contracts were full of confusing legalese

So what happens when a party violates the terms of a contract to which they’ve agreed to?

Before we can answer that question, we will need to understand the legalese or legal language used and the different clauses that appear on each contract.

Trial Period / Probation Period Clauses

This clause may have something similar to:

***This agreement is subject to a trial period of 1 month.****

***The First Six (6) months of the employment contract shall be deemed a ‘probation period’. During this probational period each party of the contract shall be entitled to terminate the contract within 30 days notice to the other party.***

This clause should be pretty easy to understand. The company wants to have at least 30 days to up to 60 days to decide whether they want to keep the new employee and be able to terminate such employment contract within the specify time. If the employer does not terminate the contract within the 6 months, the contract is fully enforceable and only via court order or mutual agreement with the employee can this contract be terminated.

gamboa

At Will – Only applicable to the United States.

Many of us who has worked and lived in the USA have seen the so called ‘at will’ contracts.

At-will employment is a term used in U.S. labor law for contractual relationships in which an employee can be dismissed by an employer for any reason (that is, without having to establish “just cause” for termination), and without warning. -Wikipedia

Such a clause may read as follow:

***Employment at ABC Corporation is “at-will.” This means that you may resign at any time, for any reason or for no reason, or with or without notice. Similarly, ABC Corporation may terminate your employment at any time for any reason or for no reason, with or without notice. This offer letter is not a contract of employment for any purpose or duration. ABC Corporation reserves the right to change its policies and procedures, with the exception of its “at-will”
policy, at any time, with or without notice.***

Example 2 – At Will Clause

***Employee acknowledges that the employment relationship created by this agreement is at will and is, to the extent set forth in this agreement, temporary in nature and can be terminated at any time by Company for any reason whatsoever.***

gamboa

 

 

 

 

Termination Clause

While most At-Will contract clearly specifies that a contract can be terminated with or without reason and at any time, most employment contracts provide a clear end date.

For temporary and limited short-term contracts, there should be an end-date, for example:

***This agreement shall continue in force from the dd Month year until the dd Month year or until earlier terminated in accordance with this agreement.***

Example 2:  Common in Employment contracts

The project agreement is entered into for a fixed term and is valid for the duration of 6 months and therefore ends automatically and by operation of law, without written termination being necessary for that purpose, on <end date>.

***Either party can immediately end the employment, without a termination period and without regard to the conditions regarding termination in the event of an urgent reason, as stated in articles <xxxx> and <xxx> of the Country Civil Code.***

***The employment contract will end on the last day of the month prior to the month in which the employee reaches the retirement age.***

On part II, we will cover the confidentiality agreement clause, and some other benefits clauses added to the employment contract like bonus, relocation, job responsibilities, competing clauses, and more.

Conclusion:

Most Europeans have what we called ‘Employment Contracts’ whereas Americans have ‘At Will’ contracts. The main differences between having an European contract versus an American contract are: stability (notice period is a must) and financial compensation (severance package equals one month salary for every year you worked for the company). Usually, an employer or employee must give 3 months notice before terminating the employment contract. Americans can be terminated the same day they report to work without notice. In the other hand, Americans make more money. For example, a clinical programmer for a pharmaceutical industry could make over 80,000/year salary depend upon experience while someone from the UK will be making less than 50,000 pounds or an EU employee less than 70,000 euros/year.

Americans make the money up front which means Americans should be saving on average one month salary per year while Europeans will receive a final check , usually one month salary for every year they have worked for the company, as part of their exit package or termination of employment also knowns as ‘severance payment’.

Need legal advice in Panama? Relocating to Panama for work and need legal advice? Panamanian work visas?

Contact Us here: Panama Lawyers and click on Consultation Form

Motto of the Month – Prove Them Wrong

I don’t need to defend myself from people that doubt me. I let my actions speak for me. – Tom Brady

Tom Brady MottoTom Brady’s 10 Rules For Sucess

  1. Earn Your Success
  2. Care Deeply
  3. Love the Game
  4. Live in The Present
  5. Execute Well Under Pressue
  6. Believe in Yourself
  7. Set your Priorities
  8. Earn it Every Single Day
  9. Evolve
  10. Stay Hungry, Stay Humble

10 Rules of Sucess

Are you Fit for 2022?

For many of us “being fit” means “being able to provide for one’s own life and wellbeing”.  So what does it mean when I company tells you we need to be fit for 2022?

Fit for 2022 is a fancy term for ‘we need to lay-off X people more if we want to make it to 2022’.

So stay healthy, stay fit and work on your skills as you never know when your company will start their ‘lay-off plan’ with a fancy word like ‘fit for 2022’.

 

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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“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.”

Eliminating Informed Consent on Human Experimentation with Vaccines and Drugs

The 21st Century Cure Act would demand that the Food and Drug Administration (FDA) add an exemption from informed Consent requirements for those clinical trials that pose no more than minimal risks and the appropriate safeguards protecting the right, safety and welfare of subjects.

Informed Consent Waiver

The above can be found in section 3024 – Informed Consent Waiver or Alterations for Clinical Investigations.

So what they are saying now they don’t have to obtain informed consents to test vaccinations or drugs on humans beings if it has been determined that the proposed pose no more than minimal risks.

Let’s review the Exemption  for Devices for Investigational Use

(g)(1) The purpose of this section to encourage to the extent consistent with the protection of public health and safety and ethical standards, the discovery and development of useful devices intended for human use and to that end to maintain optimum freedom for scientific investigators in their pursuit of that purpose.

In other words, you can get an exemption for certain conditions.

Question: if you don’t have informed consent in clinical trials experimentation on people, then how does anyone knows you are not part of an experiment?

If sponsors and clinical researchers not longer has to tell you that you are part of it or get your consent to informed you what they are doing? That may sound a little crazy.

Further source of research:

The 21st Century Cures Act Implications

Say Goodbye to Vaccine Safety Science by Barbara Loe Fisher

-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.”