I am trying to analyze the differences between two groups in the result of a psychometric survey after they have been affected by a traumatic event. The survey they have filled in consists of multiple standard psychometric scales designed to measure stress symptoms. Group A has taken the survey at 14 months after the event and then retaken the same survey after 36 months. The mean scores in the later survey is lower but that is expected since the effect of the event is expected to decrease with time. I think it is a reasonable assumption that the scores decrease in a linear way.

The trouble is that group B has taken the survey at 21 months from the event wich make the two groups not directly comparable. My goal is therefore to adjust the scores for group A so I can make a regression model with demographic and other factors as covariates and the scores of both groups at 21 months as the dependent variable.

My first idea was to simply take the proportional change in scores for each month between the 14 months and 36 months study and then adjust the scores observed at 14 month down to what they could be expected to in 21 months. I think this is fine if the scores are truly continious and a linear slope is assumed. However I think there are some problems with the first assumption.

First, the scores are not truly continious since they are the sums of yes/no questions for diffrent symptoms where "yes" is scored 1. Since the total score for an individual is supposed to measure stress and the questions are not weighted in any way the total sums can't be treated as really continious.

Furthermore the researcher I work with wants to dichotomize the total scores before analysis according to cutoffs that are standard in her field. (The whole questionaire and scoring system is a standard psychometric scale and is supposed to be treated like this). She then want to use logit analysis to estimate odds ratios. The trouble with this is that many of the individuals have exactly the score above the cutoff. If I then use the adjustment i wrote about above (meaning an adjustment of about 0.8 times the 14 months scores) theese individuals would be put below the cutoff and the incidence of stress symptoms would go from something like 40% to about 25%. Just by intuition I think this would be wrong since this drop seems way to large when the underlying scores only where supposed to drop 20 %.

I understand that there is impossible to give a clear answer for the but I would be happy to hear your experience if you encountered similar problems.

Anothed thing I was thinking about was if I could use a mixed model and set the estimates to show at 21 months. Then I dont have to adjust the scores at all but maybe this would be wrong since there is only one measurement. for the B group and no data for 21 months for the A group.

All comments are greatly appreciated.