Un-Break My Heart

A tribute to one of my favorite singers ….

And who didn’t dance to this song? I can still remember the old good days.

This is my absolute favorite remix of this song.

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Farewell to a Beloved Pet

Our dogs are more than just pets to us.  My dogs are my best friends, they are part of the family – a very small family I have known. They’re our secret keepers and playmates. Nobody loves us more than our dogs. That is why we give them everything they need to make them happy and to make their lives comfortable.

But their lives are short, and unfortunately for us, the time will come when we have to say goodbye to them. This is the hardest part of an animal lover’s life–saying goodbye to a beloved furry friend.

For Champ, a 12-years old White Westie, his new home made him so sick, I had to spend over $4000 dollars to save him or the alternative was to put him to sleep. He didn’t want to drink the local water and for some reason and all of the sudden, he could not poo or pee.

“If Love could have saved you, you would have lived forever.” David Ellison

Our dogs may not be in our lives long, but the impact they have in that short time stays with us forever. If you’ve ever lost a beloved dog, you know that loving a dog is easy, losing a dog is hard, and saying goodbye to them is the most difficult part of all.

Ik hou van je en ik mis je heel erg

 

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

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

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

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

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

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

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

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

Are you still excited about becoming a data scientist?

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

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

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

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

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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 on 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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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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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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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;

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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.

 

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

 

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

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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.”

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.”

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.

 

Opportunity to Forgive

Forgiveness is simply a process where we seek to eliminate from our mind and heart all the resentment and hurt that we are holding in our being because of what someone said or did towards us.

I was told that healing is closure. It is time for that closure otherwise I will carry out that karma until I do so. Today, I am releasing my forgiveness so we can finally move on and break this karmic pattern.

Today I choose to send love energy to those who hurt me and who are hurting…

Forgiveness is something we have to find within ourselves.

#lettinggo #forgiveness #powerofmanifestation

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.”

Hallo From The Other Side Adele

Fuzzy = Hallo

╔┓┏╦━━╦┓╔┓╔━━╗

║┗┛║┗━╣┃║┃║ X X ║

Hello, it’s me I was wondering if after all these years you’d like to meet To go over everything They say that time’s supposed to heal ya But I ain’t done much healing

Hello, can you hear me? I’m in California dreaming about who we used to be When we were younger and free I’ve forgotten how it felt Before the world fell at our feet There’s such a difference between us And a million miles Hello from the other side I must have called a thousand times To tell you I’m sorry for everything that I’ve done But when I call you never seem to be home Hello from the outside At least I can say that I’ve tried To tell you I’m sorry for breaking your heart But it don’t matter, it clearly doesn’t tear you apart Anymore

Hello, how are you? It’s so typical of me to talk about myself, I’m sorry I hope that you’re well Did you ever make it out of that town Where nothing ever happened? It’s no secret that the both of us Are running out of time So hello from the other side (other side) I must have called a thousand times (thousand times) To tell you I’m sorry for everything that I’ve done But when I call you never seem to be home Hello from the outside (outside) At least I can say that I’ve tried (I’ve tried) To tell you I’m sorry for breaking your heart But it don’t matter, it clearly doesn’t tear you apart Anymore (Highs, highs, highs, highs, lows, lows, lows, lows) Anymore (Highs, highs, highs, highs, lows, lows, lows, lows) Anymore (Highs, highs, highs, highs, lows, lows, lows, lows) Anymore (Highs, highs, highs, highs, lows, lows, lows, lows) Anymore Hello from the other side (other side) I must have called a thousand times (thousand times) To tell you I’m sorry for everything that I’ve done But when I call you never seem to be home Hello from the outside (outside) At least I can say that I’ve tried (I’ve tried) To tell you I’m sorry for breaking your heart But it don’t matter, it clearly doesn’t tear you apart Anymore

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Op deze belangrijke datum…Sacrifice Anouk

 

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Change – Amsterdam

At the end of 2008 and close to the holidays, a friend of mine from the States told me to visit this psychic lady from downtown (she was the one interested in having a palm reading). I don’t believe it much so I went along. After her reading, the lady asked me if I wanted a palm reading. I said, “why not.” Nothing to lose as I am already here.

Amsterdam

All I can remember her saying ”…soon you will meet someone and move…I see a lot of water surrounding this place…lots of water…” I said to myself. “That must be Panama.” We have the canal, rivers, lakes and two oceans…When her last words were “I see a trip to Europe”.

That was a day like today in 2009.

My life changed a day like today when I received an email that changed my life forever. A move to Europe [Amsterdam] that I never saw coming. Life has a lot of ups and downs. Like a roller coaster, I was up and many times down but it made me stronger than ever and open my life to the reality of love.

 

In front of the place I met my destiny – All is Change – Soulmate!

Life has a lot of twist and turns…

So what’s a soul mate? I was told that my past relationship was of a soul mate type.  Someone I spent at least one past life together. It sure felt more like a karmic relationship. I needed to pay a karmic debt.

One thing is clear to me. My soulmate must be in the same ‘vibrational path’ before we meet again. Whether in this life or the next (I hope not to be back to this planet… :-)…if there is no vibration, it will never happen.

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EMA and The Netherlands Biopharm Opportunities

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

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

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

Amsterdam – EMA

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

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

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

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

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

Tot Ziens!

Source:

Biospace

EMA

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

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