i'm currently designing a pilot trial to test an intervention in
hypogonadal men with non-alcoholic steatohepatitis (NASH). the primary
outcome is the change in histological grade of liver steatosis at 12
months. this is measured on a histology index score of 0 to 4 which
translate to a change score of -4 to 4 (9 point scale). negative
change is related to positive outcomes and vice versa.
here is my question;
what is the most efficient statistical method to analyse this outcome
which is on a 9 point scale (-4 to 4) ?.
of course minimising baseline imbalances in critical here. patients in
lower liver grades can only experience restrictive negative change;
e.g grade one patients can only experience a change of -1. similarly,
grade 4 can only experience negative change or stay the same.
the analysis method will definitely affect the sample size calculation
(in future definitive trial) and information required for this pilot
proposal
your help will be greatly appreciated forks.
with thanks in advance
Munya
--
from the best of my medical knowledge,histological grade of liver
steatosis is assessed through liver biopsy. i don't have a lot medical
details on how they interpret the biopsy and classify into 5
categories (0=absence of fibrosis, stage 1=perisinusoidal or
portal, stage 2=perisinusoidal and portal/periportal, stage 3=septal
or bridging fibrosis, and stage 4=cirrhosis.) i'll try to dig out more
on the classification process.
with many thanks
Munya
my initial though was re-classification of the difference into
(deteriotating(>=1 change), no change, moderate improvement (-1),
marked improvement(>=-2)). it seems it's unlikely to expect a change
of at least -3 points within a year. of course a deterioration of at
least 2 points could be expected. i could even combine the last two
categories depending on the proportion expected to have at least 2
point improvement. i'm aware about issues of losing information by
categorisation but i thought this is a scenario where you're stuck
between the walls. in essence, i won't be losing a lot of information
if a positive change of at least 3 points is "unlikely".
i would then use ordinal regression (proportional odds or something else).
is this attractive?
with thanks
Munya
>>> Frank Harrell <f.ha...@vanderbilt.edu> 7/15/2011 2:09:33 PM >>>
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--- snip ---
The proportional odds model is not adversely affected by having cells
containing only one subject. You can even use the PO model to
efficiently analyze continuous Y when there are no ties.
Frank
-- Bruce Weaver bwe...@lakeheadu.ca http://sites.google.com/a/lakeheadu.ca/bweaver/Home "When all else fails, RTFM."