PaT Plot tool for randomised trials
This page is about PaT Plot, a software tool for creating graphic depictions of randomised trials.
In randomised controlled trials, interventions may have several components, for example interviews, leaflets, group educational sessions, information packs, etc.
This simple program will enable you to depict any randomised trial graphically however many components the interventions have. It will standardise the construction process and ensure every step is covered. The PaT Plot tool is based on an original article published in the BMJ.
Once created, a PaT Plot is easy to interpret and allows clear comparisons between different arms of a trial.
Direct access the PaT Plot tool
(takes you to to the Physiology Department’s page hosting the PaT Plot tool)
(If the link above does not work try clicking here, and use public for both the User and Password login fields.)
In randomised controlled trials, interventions may have several components, for example interviews, leaflets, group educational sessions, information packs, etc.. This simple program will enable you to depict any randomised trial graphically however many components the interventions have. It will standardise the construction process and ensure every step is covered. The final PaT Plot will be easy to interpret and will allow clear comparisons between different arms of a trial.
Just follow this simple process:
1. Enter your report title
2. How many treatment arms does the trial have?
Each arm has its own column
Double click on the wording at the top of each column to title that arm/column of the trial. You can position the text by clicking on the alignment buttons in the control panel.
3. Consider important time points in the trial such as randomisation, baseline, week 1, month 2, 6 months, etc.
Information is added in rows for each time point.
Once the first row/time point appears you can customise it for your trial:
a) Does the first component in the trial happen before randomisation? (e.g. a contact that will assess patients for eligibility, or necessary training for people delivering the intervention). If so, you would need a time point before randomisation. If not, the first time point will be randomisation. Enter the time point label (randomisation or other).
b) At each time point (except randomisation and the outcome point), a component (or more than one) will be delivered. When no components are delivered, use text to describe the event (e.g. randomisation, outcome measures).
4. Consider the components of your interventions
Does the intervention involve the use of a tool which is a fixed or an inanimate object (e.g. questionnaire, reading material, letter, workbook, audiotape, computer programme, mobile phone)? If yes then these components are represented by a square.
Does it involve an activity (e.g. interview, phone call, course, individual education session or appointment, mentoring)? If yes then these components are represented by a circle.
5. Annotate your components used so far in the Component Description Table
6. The process (3-5) can now be repeated for each time point
Add components already used (highlighted in black in the right hand toolbar) or allocate new ones.
Note that you can change the width of the component display table and/or the component description table by reducing the size from 100%.
Work in Progress
If you need to leave a PaT Plot unfinished and come back to it later then it is possible to save it to your own computer. Simply click ‘save’ on the control buttons and a window will come up for you to choose where you wish to save it to.
Note: The filename used to save your plots must not include spaces, and the extension .obj should be added (e.g. “filename.obj” is acceptable, “file name.obj” or “filename” will save it but you will not be able to retrieve it again.)
When you want to return to the plot click on ‘open’ in the control buttons and select your file.
Please note that any saved plots will append to the current plot on screen, this can simply be avoided by ensuring the screen is ‘clean’ i.e. ready for a new plot.
Once the all the data are entered into the model the PaT Plot can be generated.
Reports can be produced in Word, pdf or HTML.
Once the finished report is in Word format it can be modified (e.g. to change column widths, merge cells, add brackets, etc.). Please note that, whilst plots can continue to be saved and subsequently updated, any modifications in the Word documents cannot be imported back into the computerised plot generating process.
All generated reports are saved in the public ‘List of Reports’ on the log in page. To access these at a future date you will need to double click on ‘list of reports’ before you login.
To obtain a personal username/password, please email to email@example.com
There are still some software issues at the moment which are currently being addressed. In particular, if you try to open more than one working report at a time then the two reports become merged rather than appearing as two separate windows.
PaT Plot example using the tool – from a participative intervention: the DiGEM trial
This is an example PaT Plot taken from real-life data. The DiGEM (diabetes glycaemic education and monitoring) trial took place in non-insulin treated patients with type 2 diabetes in primary care and evaluated whether self monitoring of blood glucose was effective. This trial aimed to assess whether blood glucose self-monitoring, either alone, or with training in interpreting and using measurements to guide behaviour, was more effective than usual care in improving glycaemic control. At the time, self monitoring in these patients was increasing despite inconclusive evidence of its effectiveness, and the costs of the consumable test strips used in self monitoring had become a major proportion of health care budgets.
Impact of self monitoring of blood glucose in the management of patients with non-insulin treated diabetes: open parallel group randomised trial BMJ (2007) 335(7611):132
Farmer A, Wade A, Goyder E, Yudkin P, French D, Craven A, Holman R, Kinmonth AL, Neil A
PaT Plot for DIGEM trial
Graphical method for depicting randomised trials of complex interventions
BMJ (2007) Jan 20; 334 (7585) : 127-9
Perera R, Heneghan C, Yudkin P
Tong joined Medical Sciences Division Learning Technologies Group (http://msdlt.medsci.ox.ac.uk) in 2007 after graduate from University of York with a Master’s degree in Software Engineering. Before that, he developed/co-developed several large-scale web projects for telecommunication companies.
His main interest is providing development foundation/back-end support for applications in E-Learning, Research or Administration areas. He is very keen on promoting Open Source paradigm to Educational Software Development.
Please feel free to forward the applet link to wider users.
To send feedback about this tool or ask the development team questions, please contact is at firstname.lastname@example.org. Any email sent to this address will be forwarded to email@example.com, firstname.lastname@example.org and me.
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