@Anranyu wrote:
I have 2 continuous covariants, A and B, 1 categorical covariant C, and 2 treatments Z1 and Z2, and the values of 1 parameter and its log10() value.
My question is:
1. Why we should do the ANCOVA analysis, since there are only 2 treatments?
2. May I ask the code about that? I saw the SASHEP, and it only gives proc glm procedure. But I saw most people use proc mixed.
3. How to get the GLSM, I saw some senior said "Exp(mean(log(x))) = geomean(x)", but I am still confused how to write the code, and whether I should use the original value or log10() value.
1. There are only 2 treatments indeed, but since you need to take the influence of covariates into account, you have to fit a model to the data. (It's beyond the scope of a simple hypothesis test)
2. What is SASHEP?
Here's about "PROC MIXED Contrasted with Other SAS Procedures".
https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_mixed_overview03.htm
PROC MIXED is used (instead of GLM) for hierarchical data / multi-level data / data with group structure / non-independent data or any data that requires random effects.
Random effects can be broken down into three kinds.
- Random intercepts are individual differences in the mean across all conditions (i.e., in the model intercept).
- Random slopes are individual differences in the effect of a predictor: The size and direction of an experimental effect could differ across individuals.
- Finally, correlations between random effects are model parameters describing dependencies between random intercepts and slopes.
3. Your log10-transformed values are the dependent variable, right?
I could only find some interesting info on the ratio of GLSMs.
See this article :
( National Center for Biotechnology Information (ncbi) . National Library of Medecine (nlm) . National Institutes of Health (nih) )
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2235875/
Scroll down to GLSMRs calculations.
GLSMRs = the geometric least squares means ratio
Koen
