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Category Archives: Tips & Tricks

ESTIMATE AT COMPLETION (EAC)

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 timecost, 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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Solving Data Collection Challenges

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?

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.

 

 

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Happy Holidays from RA eClinica

Thank you very much for being a great reader in 2014. What a whirlwind year it has been with many new posts  being released.

Until the 5th of January please contact us using the usual methods found here. If you’re unable to get through via the phone then please leave a detailed message including your company name and phone number and we will return your call as soon as possible.

Once again we wish you a happy holiday season. We look forward to sharing a successful and exciting 2015 with all of our customers and readers.

RA eClinical Solutions 2015

 

 

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Project Plan: CDISC Implementation

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 CDI, Define-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? We 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.

 

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Taking productivity to the next level

1) TAKE TASKS WITH YOU

Got mobile? There are plenty of free apps and plugins available to ensure your tasks are always there for you. Pick the best and stick to it.

2) ORGANIZE YOUR EMAIL INBOX

Integrate your mobile app with your email web tool. We like Thunderbird, as it is allows you to turn an email into a task with one click. Organize those email tasks any way you like, color code them, and add due dates and reminders.

3) BREAK TASKS DOWN

Small tasks are easier to complete than bigger ones, so break big tasks into a number of smaller sub-tasks that can be completed in less than an hour. This will allow you to estimate the total time involved more accurately.

4) TRACK YOUR PRODUCTIVITY

Efficiency is up and employee independence is maintained.

Now go ahead a scheduled a bike ride on those Dutch roads (We know. it is cold but the Dutchies like their bike lines).

What tools / apps do you use and recommend?

 
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Posted by on December 10, 2014 in Project Management, Tips & Tricks

 

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Good Documentation Practice (GDP) for the EDC / SAS Developer

When writing programming codes for either validating the software or for validation checks, we often have to write comments to explain why we did something.

Since the FDA regulates computerized systems used in clinical trials under the authority of Title 21 the Code of Federal Regulations Part 11 (21 CFR Part 11) – see my other article about 21 CFR Part 11 here, we need to make sure our codes and programs are documented. As you have heard before, if it is not documented, it never happened. Nevertheless, there is no mandatory regulatory agency mandating to have to do this.

GDP is an expected practice”

So how much documentation is needed? We could get into endless discussions of when we should comment, what we should comment, and how much we should comment. I have had plenty of discussions about comments with people with various opinions on the subject.

Here’s a good documentation practice for a SAS code:

For more information please visit the original post at: {EDC} Developer

Need a clinical programmer, Data Programmers (Oracle/SAS/.NET) EDC Specialists (InForm, RDC, Rave)?

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How to write query texts – 6 template sentences

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

First of all my general guidelines.

My preference is to use no more capitals then needed. Capitals in the middle of a query text, e.g. for CRF fields or for tick box options, could distract from getting the actual question asked. E.g. compare the same query texts, with and without extra capitals. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify Stop date. (Ensure that Stop date is after or at Start date AND that Stop date is not a future date.)
Referring to CRF fields as they are shown on the CRF. To easily find the involved field(s).
I prefer to leave any ‘the’ before a CRF field referral out of the query text. For more to-the-point query texts. E.g. compare the same query texts, with and without ‘the’ before data fields. Please verify stop date. (Ensure that stop date is after or at start date and that stop date is not a future date.) Please verify the stop date. (Ensure that the stop date is after or at the start date and that the stop date is not a future date.)
Consistency in phrasing a query text can help to quickly write query texts or pre-program query texts in a structured, familiar way. That’s the thought behind the following 6 template sentences for query texts. Which you can use to help you write or program your queries.

The six ‘template’ sentences for query texts:

Please provide…

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

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

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

…., please correct.

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

…., please tick…

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

Please specify…

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

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

Good luck finding your way to structure query texts…

Source:

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

 

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