Clinical Data Management Tips

As the data crossroad centre, questions about clinical data can enter from all sides. From the Project Manager and clinical study sites to the Statistician and Medical Writer. How to streamline your team’s communication and action up on these questions? Up on inconsistencies or unclarities came across?
How to grow and benefit from the questions asked?

By sharing the question and your (re-)action taken.

But then, how could you share your lessons with your colleagues?
By visualizing your team’s approach on one A4 page.

1. As a team, start your trip through the following flowchart.
 

2. Adjust the steps taken to your own situation and principles.

3. Adjust the words to your team’s familiar phrases.

4. Adjust the colours to your company colours.

5. Start to follow the steps in daily practice.

6. And fine-tune the steps where you and your team can.

Any problem, inconsistency or unclarity about clinical data, in other words any clinical data management related question asked, is an opportunity for learning and growing.
Two requirements for gaining from any questions are that you:

1. need to listen to the sender. Taking time for the sender to hand over his/her message.
2. And secondly, to stop near the end of the road and pick up the lesson or skill learned along the way.

And please take your colleagues with you. In the passenger’s seat or in the backseats.

For you to grow yourself,

A. Write down all the questions asked during working hours since last month.

B. Divide these questions in clinical data management related categories. 1=Definitely, 2=Possibly, 3=Definitely not.

C. Which category has the highest number of questions?

D. Could you and your team think of a way to decrease the number in this category  in the future?

Good luck (re-)acting up on the questions you get,

Fine regards,

Maritza

© 2012 ProCDM

Please add the following text if you like to re-publish this newsletter: “This is an article of ProCDM. Helping people with the organization of clinical databases. Receive tips and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing via http://www.procdm.nl/pages/knowledgebase.asp.”

How does the clinical database helps to decide?

1. Through the structured way of data collection. Pre-defined CRF data collected for each participating individual at comparable time intervals.

2. By verifying that the data collected is as expected. And in case it isn’t, that reasons are present to clarify the unexpected.

3. By verifying data collected for completeness. Complete for all people participating in the study.

4. By structuring the individual participant stories (CRFs) to electronic files per measurement (datasets). E.g. all recorded adverse events from all patients in the adverse event file. Or all medication administration data for all participants in the medical dosing data file.

5. By listing all data in raw data listings. With which clinical research colleagues can track people withdrawn from the study or review measurements out of normal ranges and their consequences for people’s health.

6. By handing over the final, clean data files to Statistics people for further analysis.

7. Through the status reports that alert you of expected data not yet received. Provide enrolment rates. Show any queries not resolved yet. Indicate which data is deemed clean. Everything to help you to get a complete and clean clinical database with which true decisions can be made.

A clinical database contributes to decisions about the product under investigation in the clinical trial. Thus you need to take good decisions for the clinical database to be filled too. Overall decisions about software and hardware. About system validation and your data management organization. Decisions per individual clinical database about data collection, data verification, and database organization. And whilst performing data verification, decisions about the things noticed and your action to take. Decisions about sending queries and formulating query texts. About your action up on a received query reaction. Decisions up on questions from your client and your (re-)action up on these.

For you,

A. What kind of decisions do you encounter? Those that show directly after reading this question.

B. How do you handle those decisions? With regards to comfort, time and people involved.

C. Could you delegate, automate or capture any decision in a process flowchart?

Good luck with your clinical database decisions,

Fine regards,

Maritza

© 2012, Maritza Witteveen, ProCDM  

Dear Reader,

This article is about black box testing which I use a lot within clinical data management. It helps me to judge if system, people and procedures are able to assure quality clinical study data.
Therefore I do not exactly need to know how things are done. Instead I focus on what is done. Output versus input. Simply said, I check if what goes IN, is coming OUT as expected.
I use it within clinical studies to check accurate database preparation as well as for testing programmed edit checks. I use it for quality control activities to check accurate clinical study data handling. And for test scripts, carried out for computerized system validation.
Almost all clinical data management activities can be checked with the IN = OUT, unless… principle. 

Best regards,

Maritza

1. Working harder. Spending more time in the laboratory, in the office, at your desk. If you manage to work more, as a result, you will only get more of the same kind on your to do list. Working more is fine for a short period with a clear goal. With a week max! Any longer works like an energy-drain. Slowly leaking more and more energy, fun and possibilities out of your role.
2. Hoping that the future will contain that time for you. Well, the future hasn’t, if you do not start yourself. If you do not change, the future does neither.

1. Reserve time to sit down, without distraction to invest in your idea. Even though your regular tasks are not yet finished and urgent requests were asked, guard the time scheduled for your creative mind. If you long to make a difference, this time is equally important!

Start to reserve one to 2 hours a week and just take that time. Even though you don’t know how to begin implementing your idea, take that time to start. You need to learn (again) how to put ideas in to practice. You’ll soon notice which steps are needed next.(Phone’s still and out of sight, door shut.)

2. Create space for creativity in your head. David Allen’s Getting Things Done is a very good book to start creating space in your mind again.

What can count on your enthusiasm, your interest, within clinical research? The sophisticated technology, the ambitious people challenging themselves, the added value to patient care & health? Where do you walk that extra mile for? For what clinical results and ideas do you step up?
Once in a while I encounter these questions again. What drives me to write these articles, what attracts me in clinical research? Lately because I realized I’m not deeply within core clinical data management anymore. But I still do care for people with all kinds of different roles in clinical research. How all these various specialists together get medicines and devices approved for release. From the Principal Investigator and Research Nurse at the study site, the Monitor, Project Manager, Data Manager and Statistician to the Medical Writer of the Clinical Study Report. And all other (management) functions in between. Not forgetting the more staff like roles QA and IT people conduct.
And I do think you still experience challenges comparable to mine. Ever wanted extra space to grasp an indication, a diagnosis and its implications for people’s health? Ever longed to step up for a piece of software that could boost the lay-out and receipt of your reports? Ever wanted to acquire a SAS BASE license to use within your group? When I am enthusiastic of an idea or a capability, I easily walk extra miles to get this idea or chance heard and given feedback up on. This weeks article is therefore about how to step up for your clinical results and ideas. If you want to get heard too, this article is for you.

How to step up for your clinical results and ideas – 2 strategic steps

How to present your clinical study results and your clinical research ideas at meetings, during presentations, at tele- and video conferences etc. so that your message is received? Simply heard and given feedback up on?

Well, not by:

1. The most fancy visuals. Although gadgets, easy effort and a sense of humour can help to brake any initial ice.
2. Adjusting yourself to your audience by suddenly wearing a complete suit!, for example. Changing the look you normally spread professionally, doesn’t contribute to your story. On the contrary, it distracts and weakens your story. Even if your audience is completely new to you. So, if you normally can wear jeans and a simple jacket; take care but keep that look.
3. Changing your moves. For example your normal way of talking. Trying to speak slow whereas you normally enthusiastically speak with heights and troughs. Or trying to move your hands less, whereas your hands naturally tend to help telling your story.

Two strategic presentation preparation steps:
 

1. What exactly do you want to achieve with your presentation? Do you want higher salary scales for your department? In line with your gained additional tasks and / or responsibilities? Do you want to acquire a second, lighter EDC system for your post marketing studies? Do you want to acquire a SAS license? To create raw data listings with for your Clients? Do you want to lessen the effort spend to complete your trial’s CRFs? What do you want to gain? Where do you see an opportunity for your organization or the clinical trial results? How would that make life easier?

In fact, the one big heart felt item that GOT you step up?

2. What does your audience need to understand your message? Do they need facts and costs? Impressions? Organization impacts, FTE’s? What do they need in order to be able to decide up on your subject? Who is your audience? Is that the correct audience? Can they decide themselves or can they help you further? Should you change audience? Directly contacting these managers yourself? Or in the complete picture; resonates your idea with the organization’s core business?

Imagine being part of your audience. Take in to account people’s roles within the audience you are presenting to.

Any idea what such presentations would end in?
Feedback and support to get (part of) your idea implemented. Or a very reasonable reason to park it or skip it (for the moment). Very important, allowance to participate in the discussion of your idea. And on term, more opportunities to use your dedicated strengths.

You can check if I made a good presentation start…., if you hear me through the “Significant presence program; 8 strategic steps to succeed in sharing your clinical results and ideas!”

Wishing you space to donate YOUR research contributions,

best regards,

Maritza

© 2011, Maritza Witteveen, ProCDM

You’re welcome to re-publish this newsletter, but please add the following text to it. This is an article from Maritza Witteveen. Helping enthusiastic Clinical Research Professionals to contribute to clinical results. You can receive more articles and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing to my ezine via http://www.procdm.nl/pages/knowledgebase.asp.

Last Friday I red an article about a new book of John Tierney en Roy Baumeister: Willpower. Rediscovering the Greatest Human Strength. The article struck me because it explained why my texts so often contain words like ‘structure’, ‘format’, ‘method’ and ‘program’.
Schedules and templates to work with give me room to contribute my time to the extra ordinary. E.g. to listen carefully to Clients’ needs, to find and improve solutions, to answer complex questions. In order words; to focus up on out of ranges, strange information, and phrasing.
But the article took the benefit of structures a step further by adding that structuring the ordinary saves energy. Energy, willpower you then can use to achieve your goals. Because with structures, you do not need to consume time and energy to the WHAT, WHEN, WHY, WHO and HOW of routine steps. Taking decisions, defining when it needs to be met. No, that’s already there. The decisions left are those that specifically benefit to the particular project under subject. Content decisions. Therefore this weeks article about structuring soft skills for clinical results.

 

One of the three main tasks of data management is to translate individual subject data to logically grouped datasets ready for analysis. Study data captured in a structured format with which the statistician can work. But with what datasets can the statistician work?
In fact, with everything. Because he or she is capable of transforming your datasets to suiting datasets with the statistical software. So the question could better be, with what datasets can the statistician comfortably work? Without re-structuring the data delivered?

Well, first it is handy to know a bit more about the products statisticians deliver.

1. Tables with descriptive statistics describing the subject group under study. Overall descriptive stats for all subjects together or descriptive stats per treatment group or per gender.

N=208
Gender          Male 84 ( 40,4%)                 Female 124 (59,6%)
Age (yrs)        Mean = 56,7                         Min = 34   -    Max = 82
Weight (kg)   Mean = 78,8                         Min = 51   -    Max = 111

Tables for safety outcomes. Numbers and percentages of adverse events that occurred. Overall and per treatment group.

Adverse events                   Medication A      Medication B
                                                    (n=1205)               (n=1200)
                                         No. (%) of patients   No. (%) of patients
Gastrointestinal disorders  101 (8,4%)                            113 (9,4%)
  Diarrhea                              67 (5,6%)                               66 (5,5%)
  Nausea                               61 (5,1%)                               57 (4.8%)
Muscoloskeletal and connective
tissue disorders                   98 (8,1%)                               89 (7,4%)
  Pain in extremity                45 (3,7%)                               59 (4,9%)
  Back pain                            72 (6,0%)                               62 (5,2%)

2. Graphs to visually compare the different intervention groups under study. E.g. survival rates, pharmacokinetics.

3. Statistical tests to compare the efficacy objectives between the different intervention groups. On which the conclusion of your clinical study report will be based.

“Subjects receiving the new medicine were significantly
more likely to respond well up on overall quality of life, than were those who received the placebo(P < 0,05), whereas those walking within 24 hours after surgery, or weight loss were no more likely to respond well than those without these features.”

4. And last but not least, raw data listings if not already created by data management.
(The advantage of creating raw data listings for a study is that you get to know the individual study data. You are busy with all individual data records, instead of grouping them into a table, graph or analysis. It helps to get to know the individual drop-outs, the outliers and the missing measurements.)

Subject number  Visit date       Diastolic blood pressure  Heart rate
                                                           (mm Hg)            (bpm)
1209                        13AUG2011        127                            78
1210                        15AUG2011        116                              89
1301                        16JUN2011         104                             91
         

This about the products statisticians deliver for a clinical study report. Secondly some examples of datasets and why chosen as such:

1. A demography dataset, DEMO, is delivered with all demography data for all subjects, like gender, date of birth, but also subject number and date of screening. Only this demography dataset is needed to program a descriptive stats table for all subjects.

SUBJID         DSCREEN   GENDER      DBIR
1209              12JUN2011   1               17OCT1945
1210              13JUN2011   2               10FEB1961
1301               07JUL2011   1               04DEC1954

In- and exclusion criteria can be a separate dataset. Because these are only listed and checked for deviations.

2. Datasets contain subject numbers and most of them also have visit dates. These so-called key data fields, are used to combine data from different datasets. E.g. a dataset revealing the actual treatments merged with the demography dataset. Using the subject number, both datasets can be combined. And a descriptive stats table of the subjects per treatment group can be programmed.

SUBJID         GROUP         TRTLABEL
1209               A                     New medicine
1210               A                     New medicine
1301               B                     Placebo

With exception of the key data (subject number, visit number), CRF data should exist in one dataset only. Either in this or that, but not in two or more datasets.

3. Another example, blood – and urine laboratory assessments for all visits combined in one dataset. To check for laboratory result shifts across visits.

SUBJID   DVISIT             LABP             LABR   UNIT          OUT    CS
1210        13JUN2011   ASAT              68           U/L              2          2
1210        20JUN2011   ASAT              123        U/L             1          1
1210        08AUG2011   ASAT              72          U/L             2          2
1210        15AUG2011   ASAT               52          U/L             2          2
1210         13JUN2011   Creatinine     69   umol/L             2          2

All measurements collected in one visit are not necessarily present in one dataset. On the contrary, it is more logical to have different measurements in separate datasets. Maybe a measurements dataset for small repeating measurements.

4. Datasets that needed normalization, like often is more convenient for medical history, in- and exclusion criteria and laboratory datasets, can not be combined with non-normalized data in one dataset. Normalized datasets have additional key fields next to subject number and visit number. E.g Criteria number for an in- and exclusion criteria dataset. Or a specimen (blood/urine) field and a laboratory test field for a laboratory dataset.

SUBJID   DVISIT           CRITNO    CRIT                              INEX
1210        13JUN2011  4                   BMI < 25                     Yes
1210        13JUN2011  4                   BMI < 25                      Yes
1210        13JUN2011  5                   Is the subject pregnant?  No

Thus the single outcome of the one-time measured pregnancy test at screening is often added to the demography dataset instead of added to the in- and exclusion criteria dataset.

5. For identification and search reasons, adverse event and concomitant medication datasets contain adverse event numbers respectively concomitant medication numbers.

SUBJID  CONM No.    Medication                 Reason given   AE No.
1301         23                  Atenolol                       Prophylaxis
1301         24                  Prednisone                  Adverse event    3
1301         25                  Acetylsalicylic acid  Adverse event   12
         

Do you get an idea of how to structure your CRF data in logically grouped datasets?
In practice, get the blank CRF and sit down with the statistician or statistical programmer to logically group all CRF data in datasets. The total number of datasets for a regular clinical study…. is around 20 to 30 different datasets. Estimated time to draw CRF data to grouped datasets; 30 minutes. And you will discover with what structured format the statistician comfortable works.

Kind regards, Maritza

© 2011, Maritza Witteveen, ProCDM
 

You’re welcome to re-publish this newsletter if you add the following text to it. This is an article from Maritza Witteveen of ProCDM. Data management for clinical research. Receive tips and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing via http://www.procdm.nl/pages/knowledgebase.asp.
 

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.

1. 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.)
    
2. Referring to CRF fields as they are shown on the CRF. To easily find the involved field(s).
    
3. 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.)
    
4. 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:

1. 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.

2. 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.
    
3. 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.

4. …., 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. 

5. …., 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.

6. 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,
kind regards, Maritza

© 2011, Maritza Witteveen, ProCDM
 

You’re welcome to re-publish this newsletter if you add the following text to it. This is an article from Maritza Witteveen of ProCDM. Data management for clinical research. Receive tips and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing via http://www.procdm.nl/pages/knowledgebase.asp.