# Multivariate Regression vs T-test: and implication for multiple comparisons

I have a data frame with different outcome measures (DV1:4) for participants some partcipant with additional IVs 1:2.

n=100;df <- data.frame(participant=1:n,DV1=rnorm(n),DV2=rnorm(n)
,DV3=rnorm(n),DV4=rnorm(n),IV1=rep(seq(1,2),n/2),IV1=rep(seq(2,1),n/2))


My initital thought was to use a t.test() for the different contrasts and run some post-hoc correction for multiple comparisons. I might opt for something more liberal than bonferoni correction ( What are Hommel Hochberg corrections?)

While investigating this correction I found that (Does one need to adjust for multiple comparisons when using MANOVA?) It's not necessary to correct for multiple comparisons in a multivariate model as it does it implicitly.

What would be the best option then?, to do multiple t tests or construct a multivariate model and do posthoc comparisons (of cause correcting for multiple comparisons)

I surpose that I could still control the direction of the alternative hypothesis by way of planned contrasts. And that there are different assumptions of the multivariate model to keep in mind.