Hi Ioanna,
Intention to Treat (ITT) analyses versus Per Protocol (PP - those who
complete the study as you intended, which is pretty much what you have
so far) is common in clinical trials, so it may be worth looking at the
EMEA/FDA websites and clinical trial journals for source refs. By
looking only at completers in the PP analysis, you run the danger of
biasing your results towards finding a positive result and saying a
treatment is effective, when actually it's only effective in those who
can put up with it/buy into it - that's probably where the editor is
coming from.
As Jeremy said, for the ITT you should include the data on all cases in
the group they were allocated to (I'm presuming you have two randomised
groups in this study?), regardless of whether they completed treatment.
In an ideal trial you'd still have been able to collect follow-up
assessment data even in those who withdrew from treatment. Your ITT
would tell you the effect overall, and the PP would tell you the effect
of receiving the full treatment. But you have some subjects without
assessment data? Am I correct in thinking you only have two timepoints -
baseline and end of trial (EOT)? (if you have other timepoints, I
strongly suggest including these in your analysis model). It doesn't
give you much room for imputation methods. For those where you have only
EOT, I'm not sure there's much you can do without a baseline - I'm not a
fan of substituting with the group mean, certainly not at baseline. For
those with baseline but not follow-up, a common imputation method for
ITT is Last Observation Carried Forward (LOCF), where you take the
latest post-baseline measurement for a subject and substitute that for
the missing timepoints. In your case though, if you only have two
timepoints, you don't have another post-baseline measurement to bring
forward to EOT. I doubt the editor would be suggesting you use the
baseline as the LOCF value, as that would effectively would mean 25% of
your sample would show no change (although if your intervention shows an
effect even with a quarter of your sample not changing, that might be
worth reporting!). Did the editor make reference to imputation methods,
or were they just more interested in including the data on those who
didn't receive full treatment?
Without an imputation method, it sounds like you'll have a quarter of
your data missing, so I suggest you present a comparison between those
with data for the ITT and those without, on any other variables that
could be relevant. As a reader I'd be looking to see if I can
extrapolate the ITT results to the full sample you recruited, or is
there something about these drop-outs that make them different and
unable to tolerate the treatment etc. I think the concern from the
editor is that your 'missing data' is systematically linked to
withdrawal from treatment (so not truly missing, in the random sense of
the concept) - unfortunately, with only two timepoints it's difficult to
do much to tease it apart.
I hope this rambling email helps - I'd be interested to hear how you get on!
Brian
On 06/01/2010 05:46, Ioanna Vrouva wrote:
> Dear All,
> I would be grateful for your advice concerning the following.
> I am working on an outcome analysis (with outcome data-CBCL scores-
> available at intake and end of treatment) regarding a parenting program.
> It has been impossible to obtain data from parents who dropped out (around
> 25 %). Moreover, another 15% have not provided data at either intake or
> follow-up, although they completed the intervention.
> I had previously based the outcome analysis on only those who had data
> available at both intake and completion. However, the Editor has asked me
> to present an intention to treat analysis (ITTA).
> My questions
> 1. Should I include all cases for this analysis? (regardless of whether
> they completed the treatment, and whether they provided data at both
> intake and the second time point?)
> 2. When performing an ITTA with missing data, which is the best way to
> impute the missing data (CBCL scores)? The average? Or?
> Any guidance/reading suggestions would be hugely appreciated
> Many thanks and happy New Year
> Ioanna
>
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