Ramu Sudhagoni writes:
I am working on combining three longitudinal studies using Bayesian hierarchical technique. In each study, I have at least 70 subjects follow up on 5 different visit months. My model consists of 10 different covariates including longitudinal and cross-sectional effects. Mixed models are used to fit the three studies individually using Bayesian approach and I noticed that few covariates were significant. When I combined using three level hierarchical approach, all the covariates became non-significant at the population level, and large estimates were found for variance parameters at the population level. I am struggling to understand why I am getting large variances at population level and wider credible intervals. I assumed non-informative normal priors for all my cross sectional and longitudinal effects, and non-informative inverse-gamma priors for variance parameters. I followed the approach explained by Inoue et al. (Title: Combining Longitudinal Studies of PSA, Biostatistics,2004, 483-500).
I don’t know but I’d recommend you graph your data and fitted model so you can try to understand where the estimates are coming from.
Also, get rid of those inverse-gamma priors, which aren’t noninformative at all! (See my 2006 paper.)