Intention to treat analysis

Reviewed and revised 5 July 2014


Intention to treat (ITT) analysis means all patients who were enrolled and randomly allocated to treatment are included in the analysis and are analysed in the groups to which they were randomized

  • i.e. “once randomized, always analyzed”
  • Inclusion occurs regardless of deviations that may happen after randomisation, such as:
    • protocol violations (e.g. those who received the comparative treatment or other treatments, rather than the allocated treatment)
    • losses to follow up
    • withdrawals from the study
    • non-compliance
    • refusal of the allocated treatment
  • ITT analysis preserves the prognostic balance generated by the original random treatment allocation
  • this type of analysis is in contrast to ‘per protocol’ analysis and ‘as treated’ analysis


  • supported by the CONSORT statement
  • more reliable estimate of true treatment effectiveness by replicating what happens in the ‘real world’ (e.g. noncompliance and protocol violations commonly affect therapies); treatment effectiveness is not the same as the effectiveness of an assigned treatment in an RCT
  • simplifies the task of dealing with suspicious outcomes (guards against conscious or unconscious attempts to influence the results of the study by excluding odd outcomes)
  • prevents bias when incomplete data is related to outcome
  • preserves baseline balance between groups
  • minimises Type 1 errors (‘false positives’)
  • preserves sample size (dropouts etc would otherwise decrease the sample size and decrease statistical power)
  • When the ITT and per-protocol analyses come to the same conclusions, confidence in the study results is increased


  • estimate of treatment effect is conservative because of dilution due to noncompliance and more prone to Type 2 errors (‘false negatives’)
  • heterogeneity is introduced when noncompliants, dropouts and compliant subjects are mixed together
  • does not assess treatment efficacy accurately unless there is negligible protocol violations, etc
  • protocol violations and poorly conducted trials may cause the results obtained from two different treatment groups to appear similar so ITT analysis alone is inappropriate for non-inferiority trials


‘As treated’ analysis

  • An “as treated” analysis classifies RCT participants according to the treatment that they received rather than according to the treatment that they were assigned to
  • subject to confounding in the same way as an observational study

‘Per protocol’ analysis

  • aka ‘on treatment’ analysis
  • only includes individuals who adhered to the clinical trial instructions as specified in the study protocol
  • subject to selection bias due to cross-over and loss to follow up
  • per protocol analysis may be appropriate when analysing adverse events in drug trials, as it can be argued that side-effects of actual treatment received is clinically relevant

‘modified ITT’ analysis

  • allows the exclusion of some randomized subjects in a justified way (e.g. patients who were deemed ineligible after randomization or certain patients who never started treatment)
  • definitions used are  irregular and arbitrary; consistent guidelines for its application are lacking
  • a subjective approach in entry criteria may lead to confusion, inaccurate results and bias

References and Links

Journal articles

  • Fergusson D, Aaron SD, Guyatt G, Hébert P. Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis. BMJ. 2002 Sep 21;325(7365):652-4. PMC1124168.
  • Gupta SK. Intention-to-treat concept: A review. Perspect Clin Res. 2011 Jul;2(3):109-12. PMC3159210.
  • Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012 Feb;9(1):48-55. PMC3731071.
  • Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ. 2001 Nov 13;165(10):1339-41. PMC81628.

CCC 700 6

Critical Care


Chris is an Intensivist and ECMO specialist at the Alfred ICU in Melbourne. He is also a Clinical Adjunct Associate Professor at Monash University. He is a co-founder of the Australia and New Zealand Clinician Educator Network (ANZCEN) and is the Lead for the ANZCEN Clinician Educator Incubator programme. He is on the Board of Directors for the Intensive Care Foundation and is a First Part Examiner for the College of Intensive Care Medicine. He is an internationally recognised Clinician Educator with a passion for helping clinicians learn and for improving the clinical performance of individuals and collectives.

After finishing his medical degree at the University of Auckland, he continued post-graduate training in New Zealand as well as Australia’s Northern Territory, Perth and Melbourne. He has completed fellowship training in both intensive care medicine and emergency medicine, as well as post-graduate training in biochemistry, clinical toxicology, clinical epidemiology, and health professional education.

He is actively involved in in using translational simulation to improve patient care and the design of processes and systems at Alfred Health. He coordinates the Alfred ICU’s education and simulation programmes and runs the unit’s education website, INTENSIVE.  He created the ‘Critically Ill Airway’ course and teaches on numerous courses around the world. He is one of the founders of the FOAM movement (Free Open-Access Medical education) and is co-creator of litfl.com, the RAGE podcast, the Resuscitology course, and the SMACC conference.

His one great achievement is being the father of three amazing children.

On Twitter, he is @precordialthump.

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