Briefly, intention to treat is generally the gold standard. There are exceptions to this depending on your outcome measure, but as a general rule you should include data analysed on an intention to treat basis.

Jack Henry Agree with Jack; it’s best to include trials which perform intention-to-treat analysis (ITT) vs per-protocol analysis (PP) because PP may result in bias. Exceptions would be eg subjects who although already randomised never started intervention for whatever reasons.

Shikha Sahai, a per-protocol or complete case analysis will essentially "transform" the study design from the originally-intentioned RCT to a cohort study. Thus, to preserve the key feature of RCT in question - the randomization - you have to consider the intention-to-treat analysis.

Dear Prof. Sahai!

I see there are excellent answers above. Still I want to contribute to this discussion via case research:

1) Tejpal Gupta et al. (2021). Systematic review and meta-analysis of randomised controlled trials testing the safety and efficacy of convalescent plasma in the treatment of coronavirus disease 2019 (COVID-19): Evidence-base for practise and implications for research, Transfusion Medicine Early View 29 June 2021, Free access: Article Systematic review and meta-analysis of randomised controlled...

2) Cristina Corduneanu-Huci et al (2021). The politics of experimentation: Political competition and randomized controlled trials, Journal of Comparative Economics, Volume 49, Issue 1, March 2021, Free access: Article The politics of experimentation: Political competition and r...

Yours sincerely, Bulcsu Szekely

Ref: training.cochrane.org/handbook/current.

Intention-to-treat (ITT) analysis aims to include all participants randomized into a trial irrespective of what happened subsequently (Newell 1992, Lewis 1993). ITT analyses are generally preferred as they are unbiased, and also because they address a more pragmatic and clinically relevant question.

An analysis in which data are analysed for every participant for whom the outcome was obtained is often described as an available case analysis. Some trial reports present analyses of the results of only those participants who completed the trial and who complied with (or received some of) their allocated intervention. Some authors incorrectly call this an ITT analysis, but it is in fact a per-protocol analysis. Furthermore, some authors analyse participants only according to the actual interventions received, irrespective of the randomized allocations (treatment-received analysis). It is generally unwise to accept study authors’ description of an analysis as ITT; such a judgement should be based on the detailed information provided.

Many (but not all) people consider that available case and ITT analyses are not appropriate when assessing unintended (adverse) effects, as it is wrong to attribute these to a treatment that somebody did not receive. As ITT analyses tend to bias the results towards no difference they may not be the most appropriate when attempting to establish equivalence or non-inferiority of a treatment.

In most situations, authors should attempt to extract from papers the data to enable at least an available case analysis. Avoidable exclusions should be ‘re-included’ if possible. In some rare situations it is possible to create a genuine ITT analysis from information presented in the text and tables of the paper, or by obtaining extra information from the author about participants who were followed up but excluded from the trial report. If this is possible without imputing study results, it should be done.