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"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 > Senior Statistician > Haematological Sciences > Room MG261 > Institute of Cellular Medicine > William Leech Building > Medical School > Newcastle University > Framlington Place > Newcastle upon Tyne > NE2 4HH > > Tel: (0044) (0)191 208 8142 > > You may leave the list at any time by sending the command > > SIGNOFF allstat > > to [log in to unmask], leaving the subject line blank. > -- *************************************************** J. Martin Bland Prof. of Health Statistics Emeritus Dept. of Health Sciences Seebohm Rowntree Building University of York Heslington York YO10 5DD Email: [log in to unmask] Phone: 01904 321334 Fax: 01904 321382 Web site: http://martinbland.co.uk/ Statement by the University of York: This email and its attachments may be confidential and are intended solely for the use of the intended recipient. If you are not the intended recipient of this email and its attachments, you must take no action based upon them, nor must you copy or show them to anyone. Please contact the sender if you believe you have received this email in error. Any views or opinions expressed are solely those of the author and do not necessarily represent those of The University of York. *************************************************** You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.