Trend analysis with non-binary variable

Hi, I have a data set where I have a number of organs that fall into 5 treatment groups. These treatment groups can be organized based on increasing dose (0, 1, 2, 3, 4), with 0 being my vehicle or placebo control. I have about 10 organs per treatment group. I then have 6 different characteristics that have been measured in the organs. So for each ovary, I have a % occurrence of each characteristic.

What I'm trying to do, for a given characteristic, is test to see if there is a significant trend for increasing occurrence with increase dose. I've never done this sort of analysis before, so looking for some suggestions. I stumbled upon the Cochrane-Armitage Trend Test, which seems to be just what I'm looking for, except my result variable needs to be binary - ie, yes/no. Instead, my response variable for each characteristic is a percentage, somewhere from 0-100%.

I've compared each group to the placebo individually with an ANOVA, but was looking to add in this analysis as well. Any feedback would be greatly appreciated. Thanks!
Hi, are the dose-values on a continuous scale? I.e., is dosis 4 really 4 times the amount of dosis 1? In this case you can take for each characteristic all values of this characteristic from the different participants, and make a linear regression with the dose as predictor and the characteristic as outcome. If the outcome is approximately normally distributed, you can use simple linear regression, if not, it is a little bit more complicated: If the percentage outcome is based on counts (i.e., you can express it in terms of "number of success for a number of trials"), you would choose a Generalized Linear Model (GLM) with a Binomial distributed stochastic part, if your percentage outcome is continuos (i.e. percentage of cream in milk), beta-regression would be a common choice.

If your dose is only ordinal (i.e., you can only say that dose 3 is more than dose 2) you would use an ANOVA which allows for trend analysis, since your outcome is probably not normally distributed, the Jonckheere-Terpstra Test would be appropriate
Hey, I think so. So Dose 2 is 10 times Dose 1, Dose 3 is 10 times Dose 2, etc. All one order of magnitude off from each other.

In terms of the outcome variable, it is based off of counts. So there are 6 outcome variables, and in total, they will add up to 100% for each organ of interest. We get there by doing raw counts of multiple slides from each organ - so there is no upper limit - we don't stop at say, 100. We count everything on every slide and then use the total count for that particular organ to generate the %s for each outcome for that organ.

Does that make sense? I think all my outcome variables are normally distributed, or I was able to normalize them without too much difficulty. I'm only looking at one outcome at a time, so a change in outcome 1 based on treatment - even though outcome 1 is by default associated with outcomes 2-6. So I don't think it should matter that some are normalized vs others being not.

So it sounds like perhaps beta-regression? I can do some reading up on how I'd go about that - not familiar to me. PROC NLMIXED?

Thanks for the help!!


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I think it may help us to ensure we know what you are planning, if you can now try to rephrase the question and then put it in a model equation.

y = beta

and define these terms and how they are formatted.