Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

This page is about PaT Plot, a software tool for creating graphic depictions of randomised trials.


  • Run PaT Plot
  • How to use PaT Plot
  • Worked example
  • About the author
  • Feedback
  • Citations

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.

Run PaT Plot

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.)

How to use PaT Plot

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.

Finished Work

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

Please note!

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.

Worked example

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

Original Article

Graphical method for depicting randomised trials of complex interventions
BMJ (2007) Jan 20; 334 (7585) : 127-9
Perera R, Heneghan C, Yudkin P

About Tong Chen, author of the PaT Plot tool

Tong joined Medical Sciences Division Learning Technologies Group ( 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 Any email sent to this address will be forwarded to, and me.


Primary care-based multifaceted, interdisciplinary medical educational intervention for patients with systolic heart failure: lessons learned from a cluster randomised controlled trial
TRIALS Volume: 10 Article Number: 68 Published: AUG 13 2009 ?Times Cited: 0
Peters-Klimm F, Campbell S, Muller-Tasch T, et al.

Solar Drinking Water Disinfection (SODIS) to Reduce Childhood Diarrhoea in Rural Bolivia: A Cluster-Randomized, Controlled Trial
PLOS MEDICINE Volume: 6 Issue: 8 Article Number: e1000125 Published: AUG 2009 Times Cited: 1
Mausezahl D, Christen A, Pacheco GD, et al.

Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial
CANADIAN MEDICAL ASSOCIATION JOURNAL Volume: 181 Issue: 1-2 Pages: 37-44 Published: JUL 7 2009 ?Times Cited: 1
Holbrook A, Thabane L, Keshavjee K, et al.

What works for whom, when and how?
PAIN Volume: 143 Issue: 3 Pages: 167-168 Published: JUN 2009 Times Cited: 0
Seers K

A Statistical Perspective on the Design of Drug-Court Studies
EVALUATION REVIEW Volume: 33 Issue: 3 Pages: 257-280 Published: JUN 2009 Times Cited: 0
Merrall ELC, Bird SM

A Review of Music-based Intervention Reporting in Pediatrics
JOURNAL OF HEALTH PSYCHOLOGY Volume: 14 Issue: 4 Pages: 490-501 Published: MAY 2009 Times Cited: 0
Robb SL, Carpenter JS

Serial offending: evaluation of drugs courts
LANCET Volume: 373 Issue: 9671 Pages: 1231-1233 Published: APR 11 2009 Times Cited: 0
Bird SM, Merrall ELC

Single versus multicomponent intervention in frail elderly: Simplicity or complexity as precondition for success
JOURNAL OF NUTRITION HEALTH & AGING Volume: 12 Issue: 5 Pages: 319-322 Published: MAY 2008 Times Cited: 0
Vliek S, Melis RJ, Faes M, et al.

Can an EASY care based dementia training programme improve diagnostic assessment and management of dementia by general practitioners and primary care nurses? The design of a randomised controlled trial
BMC HEALTH SERVICES RESEARCH Volume: 8 Article Number: 71 Published: APR 2 2008 ?Times Cited: 2
Perry M, Draskovic I, van Achterberg T, et al.

Diagnosis and treatment of musculoskeletal chest pain: design of a multi-purpose trial
BMC MUSCULOSKELETAL DISORDERS Volume: 9 Article Number: 40 Published: MAR 31 2008 Times Cited: 0
Stochkendahl MJ, Christensen HW, Vach W, et al.

Improving patient adherence to lifestyle advice (IMPALA): a cluster-randomised controlled trial on the implementation of a nurse-led intervention for cardiovascular risk management in primary care (protocol)
BMC HEALTH SERVICES RESEARCH Volume: 8 Article Number: 9 Published: JAN 14 2008 Times Cited: 3
Koelewijn-van Loon MS, van Steenkiste B, Ronda G, et al.

Evaluating complex interventions
WORLDVIEWS ON EVIDENCE-BASED NURSING Volume: 4 Issue: 2 Pages: 67-68 Published: 2007 Times Cited: 2
Seers K

Effectiveness of policy to provide breastfeeding groups (BIG) for pregnant and breastfeeding mothers in primary care: cluster randomised controlled trial
BMJ (2009) 338: a3026-a3026
Hoddinott P, Britten J, Prescott GJ, Tappin D, Ludbrook A, Godden DJ

Secondary prevention clinics for coronary heart disease: a 10-year follow-up of a randomised controlled trial in primary care
Heart (2008) 94: 1419-1423
Delaney EK, Murchie P, Lee AJ, Ritchie LD, Campbell NC

Is Office-Based Counseling About Media Use, Timeouts, and Firearm Storage Effective? Results From a Cluster-Randomized, Controlled Trial
Pediatrics (2008) 122: e15-e25
Barkin SL, Finch SA, Ip EH, Scheindlin B, Craig JA, Steffes J, Weiley V, Slora E, Altman D, Wasserman RC

What is missing from descriptions of treatment in trials and reviews?
BMJ (2008) 336: 1472-1474
Glasziou P, Meats E, Heneghan C, Shepperd S

Extending the CONSORT Statement to Randomized Trials of Nonpharmacologic Treatment: Explanation and Elaboration
Ann, Intern. Med. (2008) 148: 295-309
Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P for the CONSORT Group

Towards evidence based medicine for paediatricians
Arch. Dis. Child. (2007) 92: 644-644
Phillips B