[R-sig-ME] longitudinal with 2 time points

[Marc Schwartz]

Hi,

I'll throw in a reference that covers some of these issues:

Statistics Notes
Analysing controlled trials with baseline and follow up measurements
Vickers and Altman
BMJ. 2001 November 10; 323(7321): 1123–1124.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1121605/


The basic model specification would of course be:

  lm(4Wks ~ Baseline + Group)

You will also want to test for an interaction between the baseline score and your grouping factor, in case the observed group (eg. treatment) effect is dependent upon the value of the baseline measurement. In this case, unlike in the above paper, you of course end up with crossing fitted regression lines, rather than parallel lines.

HTH,

Marc Schwartz


On Aug 11, 2010, at 7:34 AM, Charles E. (Ted) Wright wrote:

> Keep in mind that running an ANOVA on the difference is not the same thing as using the baseline data as a covariate in an ANOVA on the Week 4 data. Essentially the ANOVA on the differences is like the ANCOVA with the slope constrained to be 1.
> 
> Ted Wright
> 
> On Wed, 11 Aug 2010, John Maindonald wrote:
> 
>> All these are possibilities, except maybe making baseline measurement
>> a random factor.  This would make sense only if data divide into groups,
>> and you want the baseline effect to vary randomly from group to group.
>> That may limit your ability to estimate parameters that are of interest.
>> In most circumstances that I am familiar with, it makes better sense to
>> treat baseline effect as fixed.
>> 
>> John.
>> 
>> On 11/08/2010, at 8:11 AM, array chip wrote:
>> 
>>> Hi, I am wondering if it is still meaningful to run a mixed model if a
>>> longitudinal dataset has only 2 time points (baseline and week 4)? Would it be
>>> more appropriate to simply take the difference between the 2 time points and run
>>> ANOVA (ANCOVA) on the difference? what about still running mixed model on the
>>> difference of the 2 time points, but adding baseline measurement as a random
>>> factor?
>>> 
>>> Thanks for sharing your thoughts.
>>> 
>>> John