I'm interested in conducting a meta-analysis of mental health in the first year after an event. I have a set of prospective studies that assess mental health at baseline (i.e., immediately after the event) and then at a variety of follow-up time points among the people that experienced the event (e.g., one month, six months). Some studies also include a comparison group, although I'm mostly interested in changes within the group that experienced the event. Ultimately, I would like to be able to speak to trajectories of change in various disorders after this event- for example, do rates of depression decrease significantly by 3 months and level out? Does anxiety decrease immediately?
My dilemma is, some studies assess mental health as a binary variable (i.e., number of people who have the disorder or do not have the disorder), while others assess it as a continuous variable (i.e., mean scale score/SD). I'm familiar with the single-group, pretest–posttest change/raw score effect sizes to look at changes over time for mean data (Morris & DeShon, 2002) but I'm not sure how to handle this for the dichotomous data. Ideally, I'd like to be able to find a common effect size statistic so I can analyze both types of data together. Is this possible? If so, what effect size can I use? If I have to analyze the binary data separately, can I just use an odds ratio?