Re: Estimating heritability with censored data

From: Sandra Eady <Sandra.Eady_at_CSIRO.AU>
Date: Fri, 28 Jul 2006 17:25:13 +1000

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
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

>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

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

> 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
in a particular week. Technically, we may want 'prob of dying in a
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
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.

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

Arthur Gilmour PhD
Principal Research Scientist (Biometrics)
NSW Department of Primary Industries
Orange Agricultural Institute, Forest Rd, ORANGE, 2800, AUSTRALIA

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Received on Wed Jul 28 2006 - 17:25:13 EST

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