Re: Estimating heritability with censored data

Thanks Arthur. Yes they do get sick and recover. Will ponder your
suggestions over the weekend.

-----Original Message-----
From: ASReml users discussion group [mailto:ASREML-L_at_AGRIC.NSW.GOV.AU]
On Behalf Of arthur.gilmour_at_DPI.NSW.GOV.AU
Sent: Friday, 28 July 2006 5:16 PM
To: ASREML-L_at_AGRIC.NSW.GOV.AU
Subject: Re: Estimating heritability with censored data

Dear Sandra,
> I would like to estimate the heritability of resistance to bacterial
infection in grower rabbits.

>I have data on the weekly incidence of bacterial infection in grower
rabbits and mortality data
>(with post-mortem findings of bacterial infection) that looks like
this:

>Rabbit wk5 wk6 wk7 wk8 wk9 wk10
>1 0 0 0 0 1 1
                rabbit healthy for first 4 weeks then sick but alive for
last two
>2 1 dead dead dead dead dead
       rabbit sick wk5 and dead by wk6
>3 0 0 0 0 0 0
                rabbit healthy and alive every week
>4 0 0 0 dead dead
dead rabbit apparently healthy until wk8 when it was
dead

My fist question is whether DEAD always follows SICK, or do some recover
i.e. 0 0 0 1 1 0 0

> Is there a way of analysing this data in ASREML to account for the
dead
animals?

> Two suggestions have been:
1. multiple threshold model with more than two categories
2. animal model with repeated measures for each individual
but using the whole data set ? live and dead animals. But I am not sure
this properly accounts for the missing data when an animal is dead.

** ASReml can't fit a multiple threshold model.
  You could analyses a binary trait 'Dies in period wk5...wk 10'
and then a log linear model of those that die looking at probability of
dying
in a particular week. Technically, we may want 'prob of dying in a
given
week
given survived to that week'. Data could look like
Rabbit Week Sick_this_week Died_this_week
 1 5 0 0
 1 6 0 0
 1 7 0 0
 1 8 0 0
 1 9 1 0
 1 10 1 0
 2 5 1 0
 2 6 * 1
 3 5 0 0 # all 6 lines look like this except fot
Week code
 1 5 0 0
 1 6 0 0
 1 7 0 0
 1 8 1 1

This file omits animal/weeks when the animal is dead so that probability
relates to those alive
at beginning of the week.

> Another suggestion is to define the trait as number of weeks "alive
and
healthy" so that all animals have a complete dataset.

This would presumably have a stack of animals in the highest category
(never sick) which might be unsatisfactory.
An equivalent alternative would be 0 for those always healthy, 1 for
those dead, and 'weeks sick/number_of_weeks'
for the others.

These are just a few rambling thoughts late Friday. I'm sure there will
be other ideas.

ASREML 2 is now available from http://www.VSNi.co.uk

Check out my personal site http://www.cargovale.com.au/

Grace, mercy and peace to you from God our Saviour Jesus Christ

Arthur Gilmour PhD
mailto:Arthur.Gilmour_at_dpi.nsw.gov.au
Principal Research Scientist (Biometrics)
NSW Department of Primary Industries
Orange Agricultural Institute, Forest Rd, ORANGE, 2800, AUSTRALIA

fax: 02 6391 3899; 02 6391 3922 Australia +61
telephone work: 02 6391 3815; home: 02 6364 3288; mobile: 0438 251 426

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WCGALP8 13-18 Aug Brazil
Rothamsted UK 20-30 Aug
Aust Genstat Conf 5-8 Dec Victor Harbour
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Received on Wed Jul 28 2006 - 17:25:13 EST