#StackBounty: #panel-data #meta-analysis #proportion #meta-regression #longitudinal-data-analysis Meta-analyzing rates of change in one…

Bounty: 50

I’m working on a meta analysis of prospective studies assessing changes to mental health in the year following an event. For the purposes of example, say it’s the prevalence of major depressive disorder (expressed as a proportion) and the severity of depressive symptoms (expressed as a mean and SD) in the year after giving birth. To be included in the analysis, each study had to report on depression prevalence and/or severity at at least two assessment points (e.g., 1 month post-birth, 6 months post-birth). All participants in the studies gave birth- there are no comparison groups. The assessment points and depression measures are different for each study.

Ideally, the analyses would yield an expected course of symptom progression over time across studies, perhaps by generating study-specific slopes representing the rate of change in symptoms over time and then meta-analyzing them and testing moderators of the slopes. Models would be estimated separately for proportions vs. means/SDs. I do not expect the rate of change to be linear- I expect it to be high within the first month, moderate within the following few months, and negligible thereafter (so potentially quadratic, although that is an empirical question). I’m wondering:

  • Whether this is the best way to capture what I’m trying to capture.*
  • What’s the best way to generate these study-specific slopes
  • Whether the process of generating them will be different for studies contributing proportions versus those contributing means/SDs
  • How to test whether linear or quadratic slopes are more appropriate for a given study (or whether I need to use the same kind of slope across studies)
  • Whether it will be a problem that many studies only include 2 effect sizes
  • How to then meta-analyze the resulting slopes

**Note that, in a second set of models, I’m planning to separate the effect sizes into into time ranges (e.g., 0 to 1 month, 1 to 3 months) and aggregate the effect sizes falling in each range, so I’ll be able to say X% of women have major depressive disorder within 0 to 1 months, and depression is expected to be Y% severe on a 0-100 scale.*


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