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{EDC} Developer

Specializing in Pharmaceutical Technologies

Top 3 Posts at (EDC Developer)

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

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

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

1- Data Management: Queries in Clinical Trials

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

3- Why use JReview for your Clinical Trials?

Others most read articles:

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

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

Data Management Plan in Clinical Trials

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

1-types of edit checks in clinical data management

2-Rave programming

3- pharmaceutical terminology list

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

5- edc programmer

6-central design tips and tricks

Thank you for reading!

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Motivation of the Month

If-you-can-t-convince-them-confuse-them – Harry Truman

If-you-can-t-convince-them-confuse-them

 

Featured post

Inspirational Quote of the Month

Walk away from the 97 crowd. Don’t use their excuses…

Jim-Rohn-Quote-Walk-away-from-the-97-crowd-Don-t-use-their-excuses
Featured post

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.

Featured post

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.

Featured post

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.

Featured post

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.

 

Featured post

 

 

 

 

 

 

 

Success is something you attract not something you pursue. Work harder on yourself than you do on your job. If you work harder on your job you can make a living.

If you work harder on yourself, you can make a fortune.

Featured post

Thought of the Month – June 2017

I don’t embrace excuses, I embrace Solutions. – Jon Taffer

Jon Taffer motto
Featured post

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

 

Featured post

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

Featured post

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

 

Featured post

Somebody That I Used To Know

Video – Gotye

When you spend quality time with your loved ones…

lastlovenote
lastlovenote

 

 

 

 

When you turn your back on them – when you are not looking…

Tanabata Festibal
Tanabata Festival

 

 

 

 

 

 

 

A waste of love, time and money-Yvonne Koelemeijer

As hard as it is, there is an important karmic lesson to be learned from each relationship we experience in our lives. One must remember that every ending is a blessing in disguise, for there is always a new beginning that follows it. You will love again. Like they say. What you are looking for is looking for you!

 

Anastasia – Left Outside Alone

Anastacia – One Day In Your Life

Yvonne Koelemeijer

-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.” Monique Dinandt

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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.”

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Count the number of discrepancies per procedure – OracleClinical (OC)

Let’s now write a quick program to count the number of discrepancies per procedure in OC/OCRDC:

Remember to comment /**/ or ***comment here*; what the program does. It is a good clinical practice to document everything so anyone can read your program and make the necessary updates, if necessary.

proc sql;
connect to oracle(path=ocpath);
create table discr as select * from connection to oracle
(Select  p.name, pd.test_order_sn detail, count(pd.test_order_sn) count, p.procedure_id procid
from discrepancy_management dm,
procedures p,
procedure_details pd
where dm.clinical_study_id=9999
and dm.procedure_id = p.procedure_id
and dm.procedure_detail_id=pd.procedure_detail_id
and p.PROCEDURE_VER_SN=pd.PROCEDURE_DETAIL_PROC_VER_SN
and dm.PROCEDURE_VER_SN=p.PROCEDURE_VER_SN
and dm.de_sub_TYPE_CODE=’MULTIVARIATE’
group by p.name, pd.test_order_sn, p.procedure_id
order by count(p.name)desc
);
/*document your code*/
proc sql;
connect to oracle(path=ocpath);
create table name as select * from connection to oracle
(select distinct p.procedure_id procid, p.name, pd.TEST_ORDER_SN detail
from  procedures p,
procedure_details pd
where p.clinical_study_id= 9999 *replace with your studyid;
and p.procedure_status_code !=’R’
and p.procedure_id=pd.procedure_id
order by procid
);
quit;

/* merge # of discrepancies with name */
proc sort data=discr;
by procid;
run;

proc sort data=name;
by procid;
run;

data discname;
merge discr (in=d) name (in=n);
by procid;
if n;
run;

proc sort data=discname ;
by descending count ;
run;

/* print out  */
proc print data=discname label;
var name numdisc percent numdcf;
label numdisc = ‘Number of discrepancies’
numdcf = ‘Number of DCFs’;
title “Number of discrepancies per Procedure”;
title2 “RA eClnica”;
run;

You could also export the report to Excel xls and have your DM / data manager review it.

Good luck and let me know if it was helpful.

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

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Mi Alma Fantasma (My soul)


Dana Kerstein (with lyrics)

These past few years, I have embarked on a spiritual journey. That journey has ended. I found the answers to my questions (even though most of it is a painful discovery, I believe this is the end of that journey). A new cycle of my career and personal life has started and I’m looking forward to this new beginning.

There are things we can’t do alone. Argue, climb and hold the ladder at the same time, etc. We think that we should have live as a couple or with your ideal partner. Even if your partner is very strange. The fact is, there are couples that ended out like a three (3) – third-party involvement; or couples that separate; there are couple that are just simple impossible or incompatibles; or couples that can still continue as a couple due to family commitments or routine, when at some point in time, there was a couple. Couples that were and today are nothing. And that’s what scare us the most in this life. When a couple breaks down, for whatever reason, the first feeling we feel is panic. The loss of control of our life and to feel alone.

The most important thing in this life is to learn how to fly alone!

So now that I am back, I am looking forward to sharing some new insights on CDISC/SAS mapping projects, new EDC systems I am testing out and some old coding.

Note: Please note I am not longer available for consultation or business. Do not contact me via the contact on this blog. I will not return your request.

Mi Alma Fantasma Yvonne Koelemeijer

Escucho un susurro silencioso,
sé que eres un fantasma,
y observo que tu sombra tarda en marcharse…
Y siento tu fantasma…

¿Cuánto tiempo; cuán lejos necesito ir?
Antes de que tu corazón roto
se abra y tome mi alma…
mi alma…

Dentro de las oscuras esquinas de una mente atormentada
veo imágenes dispersas de un solitario niño

¿Cuánto tiempo?, ¿cuán lejos necesito ir?
Antes de que tu roto corazón
se abra y toque mi alma…
mi alma…

Y todo lo siento dentro de mi,
es un espacio vacío,
sé que deseas aparecerme
pero no dejas tu rastro..

¿Cuánto tiempo?, ¿cuán lejos necesito ir?…
Antes de que tu corazón roto
se abra y toque mi alma…
mi alma
mi alma
mi alma

-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.”

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Solving Data Collection Challenges

Cross-partnership between sponsors and CROs for the collection and analysis of clinical trial data are complex. As a result there are a number of issues encountered during the running of  trial.

As with many projects, standardization projects like CDISC is a huge undertake. It requires resources, technology and knowledge-transfer. The industry (FDA for example) has been working on standardization for years but on September 2013, it became official, in which the FDA released a ‘Position Statement‘.

 Data Collection

According to the WHO, data collection is defined as the ongoing systematic collection, analysis, and interpretation of health data necessary for designing, implementing, and evaluating public health prevention programs.

Sources of data: primarily case report books or (e)CRF forms, laboratory data and patient report data or diaries.

 Challenges of data collection

It is important for the CROs / service providers to be aware of the potential challenges they may face when using different data collection methods for partnership clinical studies. Having several clients does not mean having several standards or naming conventions. This is the main reason why CDISC is here. So why are many CROs or service providers not using CDISC standards?

Another challenge is time limitations. Some clinical trials run for just a few weeks / months.

It may be found difficult to understand the partnership in the amount of time they have. Hence, most CROs and service providers prefer to perform manual mapping at the end of the trial, hence, re-work and manual work.

Funding also plays a key challenge for CDISC-compliance data collection study. Small researchers or biotechnology companies that do not have the resources in-house, out-sourced this task to CROs or service providers and are not interested whether it is compliance as long as it is save them money. But would it save money now instead of later in the close-out phase?

Anayansi Gamboa - Data Status

 

 

 

 

 

If there is a shortage of funding this may not allow the CRO or service provider all the opportunities that would assist them in capturing the information they need as per CDISC standards.

We really don’t have the level of expertise or the person dedicated to this that would bring, you know, the whole thing to fruition on the scale in which it’s envisioned – Researcher

Role of the Library

There is a clear need for libraries (GL) to move beyond passively providing technology to embrace the changes within the industry. The librarian functions as one of the most important of medical educators. This role is frequently unrecognized, and for that reason, too little attention is given to this role. There has been too little attention paid to the research role that should be played by the librarian. With the development of new methods of information storage and dissemination, it is imperative that the persons primarily responsible for this function should be actively engaged in research. We have little information at the present time as to the relative effectiveness of these various media. We need research in this area. Librarians should assume an active role in incorporating into their area of responsibility the various types of storage media. [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC232677/]

Review and Revise

At the review and revise stage it might be useful for the CRO or service provider to consider what the main issues are when collecting and organizing the data on the study. Some of these issues include: ensuring sponsors, partners and key stakeholders were engaged in the scoping phase and defining its purpose; the objectives have been considered; the appropriate data collection methods have been used; the data has been verified through the use of multiple sources and that sponsors have approved the data that is used in the final clinical data report.

Current data management systems must be fundamentally improved so that they can meet the capacity demand for secure storage and transmission of research data. And while there can be no definitive tools and guideline, it is certain that we must start using CDISC-standards from the data collection step to avoid re-inventing the wheel each time a new sponsor or clinical researcher ask you to run their clinical trial.

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.

Featured post

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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Project Plan: CDISC Implementation

CDISC standards have been in development for many years. There are now methodologies and technologies that would make the transformation of non-standard data into CDISC-compliance with ease. Clinical trials have evolved and become more complex and this requires a new set of skills outside of clinical research – Project Management.

As with many projects, CDISC is a huge undertake. It requires resources, technology and knowledge-transfer. The industry (FDA for example) has been working on standardization for years but on September 2013, it became official, in which the FDA released a ‘Position Statement‘.

So what is CDISC? We can say that it is way of naming convention for XPT files, or field names naming conventions or rules for handling unusual data. Currently, there are two main components of CDISC: SDTM (Study Data Tabulation Model) and aDAM (Analysis Data Model).

As a project manager and with the right tool, you can look to a single source project information to manage the project through its life-cycle – from planning, through execution, to completion.

1) Define Scope: This is where you’re tested on everything that has to do with getting a project up and running: what’s in the charter, developing the preliminary scope, understanding what your stakeholders need, and how your organization handles projects.

The scope document is a form of a requirement document which will help you identify the goals for this project. It can also be used as a communication method to other managers and team members to set the appropriate level of expectations.

The project scope management plan is a really important tool in your project. You need to make sure that what you’re delivering matches what you wrote down in the scope statement.

2) Define Tasks: we now need to document all the tasks that are required in implementing and transforming your data to CDISC.

Project Tasks  (Work packages) Estimates (work unit)
Initial data standards review 27
Data Integrity review 17
Create transformation models 35

The work breakdown structure (wbs) provides the foundation for defining work as it relates to project objectives. The scope of work in terms of deliverables and to facilitate communication between the project manager and stakeholders throughout the life of the project. Hence, even though, preliminary at first, it is a key input to other project management processes and deliverables.

3) Project Plan: Once we completed the initiation phase (preliminary estimates), we need to create a project plan assigning resources to project and schedule those tasks. Project schedules can be presented in many ways, including simple lists, bar charts with dates, and network logic diagrams with dates, to name just a few. A sample of the project plan is shown below:

project plan sample
image from Meta‐Xceed paper about CDISC

4) Validation Step: Remember 21 CFR Part 11 compliance for Computer Systems Validation? The risk management effort is not a one-time activity on the project. Uncertainty is directly associated with the change being produced by a project. The following lists some of the tasks that are performed as it pertains to validation.

  • Risk Assessment: Different organizations have different approaches towards validation of programs. This is partly due to varying interpretations of the regulations and also  due to how different managers and organizations function. Assess the level of validation that needs to take place.
  • Test Plan: In accordance with the project plan and, if not, to determine how to address any deviation. Test planning is essential in:  ensuring testing identifies and reveals as many errors as possible and to acceptable levels of quality.

test plan-cdisc

  • Summary Results: This is all the findings documented during testing.

An effective risk management process involves first identifying and defining risk factors that could affect the various stages of the CDISC implementation process as well as specific aspects of the project. riskplan

5) Transformation Specification: Dataset transformation is a process in which a set of source datasets and its variables are changed to  meet new standard requirements. Some changes will occur during this step: For example, variable name must be 8 chars long. The variable label must not be more than 40 chars in length. Combining values from multiple sources (datasets) into on variable.

6) Applying Transformation: This is done according to specification however, this document is active during the duration of a project and can change. There are now many tools available to help with this tasks as it could be time consuming and resource intensive to update the source code (SAS) manually. Transdata, CDISCXpres, SAS CDIDefine-it; just to name a few.

7) Verification Reports: The validation test plan will detail the specific test cases that need to be implemented  to ensure quality of the transformation. For example, a common report is the “Duplicate Variable” report.

8) Special Purpose Domain: CDISC has several special purpose domains: CO (comments), RELREC (related records or relationship between two datasets) and SUPPQUAL (supplemental qualifiers for non-standards variables).

9) Data Definition Documentation: In order to understand what all the variables are and how they are derived, we need a annotation document. This is the document that will be included during data submission. SAS PROC CONTENTS can help in the generation of this type of metadata documentation. The last step in the project plan for CDISC implementation is to generate the documentation in either PDF  or XML format.

CDISC has established data standards to speed-up data review and FDA is now suggesting that soon this will become the norm. Pharmaceuticals, bio-technologies companies and many sponsors within clinical research are now better equipped to improve CDISC implementation.

Need SAS programmers? RA eClinica can help provide resources in-house / off-shore to facilitate FDA review by supporting CDISC mapping, SDTM validation tool, data conversion and CDASH compliant eCRFs.

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

Featured post

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”.

Featured post

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
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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.

Featured post

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.

 

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Goog Clinical Practice – The Bilble

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

Confusion of Money

You will be amazed by the everyday realities you practice and unbelievable discoveries hiding in plain sight.

Your long held beliefs will be shattered and replaced with confirmed knowledge.

And of course, you will obtain your reward….if…you can renew your mind.

The Reward: What you lost…..is restored by default.

I came across Marcus’s video even after I knew the answers as I woke up to the reality of this planet as I was looking for answers. But Marcus explained it in a more simplistic way. I never realized my property = my labor; was stolen from me for a few IOUs. Something today we call ‘money’ which is nothing more than IOUs. It has not real value. The thieves enrich themselves with our labor or property in return we receive a promise to pay – something of no real value.

Listen to Marcus ‘s video “confusion of money’ with an open-mind and comment below if you feel the same way about your lost property.

The next cycle of my life I hope to find the final answer – how to not volunteer. After all, I never signed any consent or gave anyone any authorization to take my property. It is made believe, by default, that somehow I want to be part of it. And that’s farther from the truth. There is a way out…

Money has not value until you give it value.

 

Motivation of the Month

In the absence of that which you-are-not, that which you-are, is not… With no cold we cannot know warmth; without the up we cannot know down, without “good” we cannot know “bad”. And yet, we make it all up. We decide what is “cold” and what is “warm”, what is “up” and what is “down”. We decided what is “good” and what is “bad”. The universe is a massive entity of objectives. We label them.

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