Post Hoc application of Multiple Comparisons I am working on a project which uses a publication that appeared in  print in 1998 and reported a completed double blind randomized clinical trial. 
Due to the confidential nature of the project I can't give too much detail.  The protocol for the clinical trial planned to have three co-primary endpoints. A multiple comparisons procedure was not described in the protocol. In the publication Each of the co-primary endpoints was statistically significant at the 0.05 level. 
I'd appreciate thoughts as to whether or not a "multiple comparisons procedure" (MCP) can be "retrospectively" applied to that statistical analysis in this example to a publication completed over a decade in the . 
In advance, my view on MCP's is that they should be prospectively planned in order to control Type I error and that there is no justification for retrospective application of an MCP. 
thanks in advance
 A: As the MCP does not affect the data collection in any way, the only argument against a post-hoc change of the analysis plan would be that you make this decision AFTER looking at the data, i.e. your decision about the MCP might now be (subconsciously) influenced by the knowledge of the outcomes, e.g. the p-values of the three endpoints. 
As long as you are sure that this is not the case (a good test would probably be to go to a random statistician, explain only the design, and ask what MCP are necessary), I see no problem with a post-hoc correction of the analysis. 
A different question is of course if you need a MCP in the first place. Having several endpoints / tests does not necessarily require an MCP, it depends on question and interpretation.
A: For multiple comparison procedures to control the type I error rate, they should indeed be pre-specified. The only retrospective argument that may hold some water might be that there had been no/insufficient awareness of multiple comparison issues, but that no matter what reasonable multiple comparison procedure one would have chosen the results are still always significant. For a set of reasonable (and pretty conservative) procedures, I might consider a 1/3 : 1/3 : 1/3 Bonferroni split, as well as hierarchical testing in any of the 6 possible orders.
