PolyGIM is a novel method that integrates individual-level data and summary statistics with polytomous outcomes
PolyGIM PACKAGE can be installed via Github. To install the latest version of PolyGIM package via Github, run following commands in R:
library(devtools)
devtools::install_github("fushengstat/PolyGIM")
This is a good start point for using PolyGIM package.
data(data, package = "PolyGIM")
formula = "y~score"
V = diag(length(models))
fit = polygim_v(formula, int, models, ncase, nctrl, V)
# estimate
fit$theta
# the corresponding standard errors
fit$se
PolyGIM employs a user interface similar to that of GIM. For more information on using the package, please consult the user manual and the GIM vignette.
Fu S., Purdue M. P., Zhang H., Wheeler, W., Qin J., Song L., Berndt S. I., & Yu K. (2023). Improve the model of disease subtype heterogeneity by leveraging external summary data. PLOS Computational Biology, Accepted.
Fu, S., Deng, L., Zhang, H., Qin, J., & Yu, K. (2023). Integrative analysis of individual-level data and high-dimensional summary statistics. Bioinformatics, 39(4).
Zhang, H., Deng, L., Wheeler, W., Qin, J., & Yu, K. (2022). Integrative analysis of multiple case‐control studies. Biometrics, 78(3), 1080-1091.
Zhang, H., Deng, L., Schiffman, M., Qin, J., & Yu, K. (2020). Generalized integration model for improved statistical inference by leveraging external summary data. Biometrika, 107(3), 689-703.