in economics i have seen intended to treat analysis used to maintain a clear randomization design.
to drop observations means to make some assumptions on the process leading to this dropping.
one wishes to avoid this.

one refers to ITT when not all who were supposed to be treated did get treatment, or vice versa
when some who were not supposed to be treated did get the treatement.

importantly, the deviations from the desired treatment are not decided by the person doing the study. so when one
derives the ITT effect, then this incorporates the fact (that is not under the control of the designer)
that some will not be treated even if they are intended to.

this only works if each subject has some measured outcome. so if a patient dies then one has
to be able to assign an outcome, just as if that patient had been treated or not.
karl

On 5.1.18 21:17, Martin Bland wrote:
[log in to unmask]" type="cite">
"Intention to treat" can mean different things to different people.  As the idea is to preserve the comparability given by random allocation. I think that Gupta is correct.  However, I have known pharma studies where the ITT samples are all those who receive at least one dose of the study medication.  To me this is plain wrong.  

It can be argued that any missing data will mean that the analysis is not ITT.  I think this is correct, but as it is almost always the case that something is missing it makes ITT an ideal aspiration rather than a practicality.  

In some studies, death is an adverse outcome, for example in cardiology studies, and we often use a composite outcome of death or cardiovascular event.  In others, it is not, e.g. palliative care trials.  Here there is much debate over whether we can treat death as an outcome and, if not, what we do about it.  The participants are moribund anyway and the treatment is to alleviate suffering, not to prevent death.  Can we treat the observation as missing?  It is argued that there could not be an observation of the outcome, e.g. pain score, so it is not missing.    

I think that if death or otherwise is not an expected treatment effect, we can treat it as missing data and use imputation, but my palliative care colleagues might disagree.  If it is a plausible treatment effect, either occurrence or prevention, I would have some kind of composite outcome and count it as death, even if the unfortunate participant had been run over by a taxi.

Martin

.

On 5 January 2018 at 14:58, Kim Pearce <[log in to unmask]> wrote:
Hi everyone,

Just a quick question.  In an "Intention to Treat: a review" by Gupta (2011) ( http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159210/ )  an intention to treat (ITT) analysis is said to include "all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol".  In other words, "ITT analysis includes every subject who is randomized according to randomized treatment assignment. It ignores noncompliance, protocol deviations, withdrawal, and anything that happens after randomization."  Bearing this in mind,  I assume that this means that even if a patient dies during the study (that is, after randomisation), they are still included in an ITT analysis?

Thank you for your assistance on this matter.

Kind Regards,
Kim

Dr Kim Pearce PhD, CStat, Fellow HEA
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