# Paired t-test following main effect of mixed model with missing data

I am analyzing data for a repeated measures study with missing data. For example here is a 3 X 3 experimental design with three conditions and 3-time measures: I am using a mixed model for the main effects:

 model1 <- lme(outcomeMeasure ~ condition * time, random = ~1|subject/condition/time, data = exampleData)

anova(model1)
numDF denDF  F-value p-value
(Intercept)                 1    57 892.3397  <.0001
condition                   2    21   1.6985  0.2066
time                        2    57   4.5983  0.0363
condition:time              2    36   0.1513  0.8601


Since time is significant, I would like to follow up with a paired test by averaging across condition to obtain values of each subject's time. As you can see in the example data above, subject 1 is missing an entire condition. When I aggregate the data across time:

timeSubjectMeans <- aggregate(value ~ Subject * time, data = exampleData, FUN = mean) In order to perform a paired t-test on this data, should I exclude subject 1 because they are missing 1 of the conditions?

• For the random effect part, do you know what random = ~1|subject/condition/time means? – user158565 Nov 27 '18 at 21:31
• This defines the multilevel structure of the model's random factor subject. Within each subject are the levels of condition, and within each condition are the levels of time. – hermesviatori17 Nov 27 '18 at 22:02
• We use the random effect to incorporate the correlation duo to the repeated measures. I am afraid your random specification specifies the independent among response variables. – user158565 Nov 27 '18 at 22:06
• Thank you for the response. Are you saying condition and time should not be in the random effect of the model? I've seen models that are just a random intercept for the subject: random = ~1|subject. – hermesviatori17 Nov 27 '18 at 22:15
• subject/condition/time means each obs has its own random effect? if it is true, you specified they are independent. If you use random = ~1|subject, it specifies that 9 obs from the same subject are correlated with the same correlation coefficient. The obs from different subjects are independent. It it is what you want, it is correct. – user158565 Nov 27 '18 at 22:42