Placenta efficiency is calculated as the ratio between piglet birth weight and its matched placental weight, and there is a within-litter (between siblings) variation on placental efficiency in pigs. In an experiment we opportunistically managed to obtain placental efficiency data from an average of 5 new-born piglets (accounts for approx. 36% of total number of piglets born alive) born to each experimental sow (sows were artificially mated using the semen from a single male pig and then allocated into two dietary treatments- A and B during gestation). I think that analysing the averaged placental efficiency from the 5 piglets as a trait of the sow compromises statistical power, and acutally placental efficiency is not an exlusive trait of a sow but it is also influenced by individual foetal condition.
What would be an appropriate statisical model to analyse the main effect of treatment on the placental efficiency?
An example in a publication is preferred.
Thank you.
It would depend on what you want to show.
If you are analysing the effect of dietary treatment, then this is obviously ANOVA. Otherwise, you might want to show the relationship between birth weight to placental weight (if linear: OLS model or correlation test, if not: GLM). However, you also mentioned it being influenced by foetal condition (if linear: multivariate linear model, if not: multivariate GLM).
These methods are all elementary, which are probably applied in over 90% of all (serious) academic publication in any applied science discipline.
It depends on the distribution "shape" of the objective variable (i.e. ratio between piglet birth weight and its matched placental weight).
But if normally distributed, well-known ANOVA or linear regression model are suffice since you had set clear experimental design in advance.
As you said, averaging is not a good idea, I think.
Yusaku Ohkubo ,
I do not think that it matters. The error term has to be normally distributed, but not necessarily the dependent variable. This applies to both OLS and ANOVA.
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