Multiple Primary Outcomes and Analysis Strategy

It is so common for researchers to want to include more than one outcome measure as a primary outcome in studies evaluating patient-centered interventions.  A good discussion of this situation, including both investigator/clinician and the statistical perspectives in summary, can be found here - along with a look at the literature practices in studies relating to depression as a subject area.

Quickly, the dilemma centers around the conservativeness of Bonferroni-type corrections, when outcomes are correlated, and the high false-positive rates and difficulty of interpreting results, when multiplicity is not correctly addressed.  CONSORT guidelines recommend selection of just one primary outcome, but this does not provide guidance when a single outcome is not deemed adequate.

Joint testing of multiple outcomes using, for example, linear mixed models with multiple continuous outcomes and random subject effects to account for within-patient correlation, is a method worth considering.  Yoon et. al. report here on simulation study to evaluate this approach in a several scenarios. 

This reminds me of a situation where hypotheses for multiple important outcomes were kept separate, and a multiple testing procedure for these was considered based on Rosenbaum's "testing hypotheses in order" .  Follow up on this to see what its performance (operating characteristics) looks like would be good.

Either of these methods could be used to strengthen a proposal where multiple outcomes seem to be needed.