metacart-package: R package for meta-CART
In XinruLI/metacartv0.1.0: Meta-CART: A Flexible Approach to Identify Moderators in Meta-Analysis
Description
Details
Author(s)
References
See Also
Description
In many areas, there are multiple moderators available (e.g., intervention characteristics).
In such cases, traditional meta-analysis methods often lack sufficient power to investigate
interaction effects between moderators, especially high-order interactions.
To solve this problem, meta-CART was proposed by integrating Classification and Regression Trees (CART)
into meta-analysis. The method idenfities the interaction effects between influential moderators,
splits the studies into more homogeneous subgroups, and test the interaction effects by Q-statistic.
The fixed effect or random effects assumption can be consistently taken into account in both detection and testing process.
Details
Package: metacart
Type: Package
Version: 1.0-0
Date: 2017-10-02
License: GPL
This method is suitable for identifying interaction effects between dichotomous,
ordinal, continuous, and nominal moderators.
The output of a REmrt object shows meta-CART analysis results based on the random effects model.
And the output of a FEmrt object shows meta-CART analysis results based on the fixed effect model.
The two objects display results for subgroup analysis including the Q-statistic and estimates for the subgroup effect sizes.
Furthermore the predict functions predict.REmrt and predict.FEmrt can be used to predict the effect size given the moderators.
The plot functions plot.REmrt and plot.FEmrt show the interaction effects between identified moderators.
The core functions of the package are FEmrt and REmrt..
Author(s)
Maintainer: Xinru Li <x.li@math.leidenuniv.nl>; Contributors: Elise Dusseldorp, Kaihua Liu (supported with the plot function), Jacqueline Meulman.
References
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from meta-cart analysis. Health Psychology, 33 (12), 1530-1540. doi:
10.1037/hea0000018.
Li, X., Dusseldorp, E., & Meulman, J. J. (2017). Meta-CART: A tool to identify interactions between moderators in meta-analysis. British Journal of Mathematical and Statistical Psychology, 70(1), 118-136. doi: 10.1111/bmsp.12088.
Therneau, T., Atkinson, B., & Ripley, B. (2014) rpart: Recursive partitioning and regression trees. R package version, 4-1.
The articles of our own work can be found at http://www.elisedusseldorp.nl/
See Also
FEmrt, REmrt, summary.FEmrt,summary.REmrt,
plot.FEmrt,plot.REmrt,predict.FEmrt,predict.REmrt
XinruLI/metacartv0.1.0 documentation built on May 14, 2019, 10:32 a.m.
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Description Details Author(s) References See Also
Description
In many areas, there are multiple moderators available (e.g., intervention characteristics). In such cases, traditional meta-analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high-order interactions. To solve this problem, meta-CART was proposed by integrating Classification and Regression Trees (CART) into meta-analysis. The method idenfities the interaction effects between influential moderators, splits the studies into more homogeneous subgroups, and test the interaction effects by Q-statistic. The fixed effect or random effects assumption can be consistently taken into account in both detection and testing process.
Details
| Package: | metacart |
| Type: | Package |
| Version: | 1.0-0 |
| Date: | 2017-10-02 |
| License: | GPL |
This method is suitable for identifying interaction effects between dichotomous,
ordinal, continuous, and nominal moderators.
The output of a REmrt object shows meta-CART analysis results based on the random effects model.
And the output of a FEmrt object shows meta-CART analysis results based on the fixed effect model.
The two objects display results for subgroup analysis including the Q-statistic and estimates for the subgroup effect sizes.
Furthermore the predict functions predict.REmrt and predict.FEmrt can be used to predict the effect size given the moderators.
The plot functions plot.REmrt and plot.FEmrt show the interaction effects between identified moderators.
The core functions of the package are FEmrt and REmrt..
Author(s)
Maintainer: Xinru Li <x.li@math.leidenuniv.nl>; Contributors: Elise Dusseldorp, Kaihua Liu (supported with the plot function), Jacqueline Meulman.
References
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from meta-cart analysis. Health Psychology, 33 (12), 1530-1540. doi: 10.1037/hea0000018.
Li, X., Dusseldorp, E., & Meulman, J. J. (2017). Meta-CART: A tool to identify interactions between moderators in meta-analysis. British Journal of Mathematical and Statistical Psychology, 70(1), 118-136. doi: 10.1111/bmsp.12088.
Therneau, T., Atkinson, B., & Ripley, B. (2014) rpart: Recursive partitioning and regression trees. R package version, 4-1.
The articles of our own work can be found at http://www.elisedusseldorp.nl/
See Also
FEmrt, REmrt, summary.FEmrt,summary.REmrt,
plot.FEmrt,plot.REmrt,predict.FEmrt,predict.REmrt
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