R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers:
Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total 397 372 A test of heterogeneity for a table like this could simply be Pearson' chi-square test. ------ smoke.data <- matrix(c(83,90,129,70,3,3,7,12), ncol=2, byrow=F) chisq.test(smoke.data, correct=T) > X-squared = 12.6004, df = 3, p-value = 0.005585 ------ Now this test implies that the data is heterogenous and that pooling might be inappropriate. This type of analysis could be considered a fixed effects analysis because it assumes that the 4 studies are all coming from one underlying population. But what if I wanted to do a mixed effects (fixed + random) analysis of data like this, possibly adjusting for an important covariate or two (assuming I had more studies, of course)...how would I go about doing it? One thought that I had would be to use a mixed effects multinomial logistic regression model, such as that reported by Hedeker (Stat Med 2003, 22: 1433), though I don't know if (or where) it is implemented in R. I am certain there are also other ways... So, my questions to the R experts are: 1) What method would you use to estimate or account for the between study variance in a dataset like the one above that would also allow you to adjust for a variable that might explain the heterogeneity? 2) Is it implemented in R? Brant Inman Mayo Clinic ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
