The R package CMAverse
provides a suite of functions for
reproducible causal mediation analysis including cmdag
for
DAG visualization, cmest
for statistical modeling and
cmsens
for sensitivity analysis.
See the package website for a quickstart guide, an overview of statistical modeling approaches and examples.
Cite the paper: CMAverse a suite of functions for reproducible causal mediation analyses
We welcome your feedback and questions (email bs3141@cumc.columbia.edu)!
cmest
implements six causal mediation analysis
approaches including the regression-based approach by Valeri et
al. (2013) and VanderWeele
et al. (2014), the weighting-based approach by VanderWeele
et al. (2014), the inverse odd-ratio weighting approach by
Tchetgen
Tchetgen (2013), the natural effect model by Vansteelandt
et al. (2012), the marginal structural model by VanderWeele
et al. (2017), and the g-formula approach by Robins
(1986).
cmest
currently supports a single exposure, multiple
sequential mediators and a single outcome. When multiple mediators are
of interest, cmest
estimates the joint mediated effect
through the set of mediators. cmest
also allows for time
varying confounders preceding mediators. The two causal scenarios
supported are:
(1) There are no confounders affected by the exposure:
(2) There are mediator-outcome confounders affected by the exposure and these confounders precede all of the mediators:
rb | wb | iorw | ne | msm | gformula^{1} | |
---|---|---|---|---|---|---|
Continuous Y^{2} | √ | √ | √ | √ | √ | √ |
Binary Y | √ | √ | √ | √ | √ | √ |
Count Y | √ | √ | √ | √ | √ | √ |
Nominal Y | √ | √ | √ | × | √ | √ |
Ordinal Y | √ | √ | √ | × | √ | √ |
Survival Y | √ | × | √ | × | √ | √ |
Continuous M | √ | √ | √ | √ | × | √ |
Binary M | √ | √ | √ | √ | √ | √ |
Nominal M | √ | √ | √ | √ | √ | √ |
Ordinal M | √ | √ | √ | √ | √ | √ |
Count M | √ | √ | √ | √ | × | √ |
M of Any Type | × | √ | √ | √ | × | × |
Continuous A | √ | ×^{3} | × | √ | ×^{4} | √ |
Binary A | √ | √ | √ | √ | √ | √ |
Nominal A | √ | √ | √ | √ | √ | √ |
Ordinal A | √ | √ | √ | √ | √ | √ |
Count A | √ | ×^{5} | × | √ | ×^{6} | √ |
Mediator-outcome Confounder(s) Affected by A | × | × | × | × | √ | √ |
2-way Decomposition | √ | √ | √ | √ | √ | √ |
4-way Decomposition | √ | √ | × | √ | √ | √ |
Estimation: Closed-form Parameter Function | √^{7} | × | × | × | × | × |
Estimation: Direct Counterfactual Imputation | √ | √ | √ | √ | √ | √ |
Inference: Delta Method | √^{8} | × | × | × | × | × |
Inference: Bootstrapping | √ | √ | √ | √ | √ | √ |
Marginal Effects | √^{9} | √ | √ | √ | √ | √ |
Effects Conditional on C | √^{10} | × | × | × | × | × |
rb: the regression-based approach; wb: the weighting-based approach; iorw: the inverse odds ratio weighting approach; ne: the natural effect model; msm: the marginal structural model; gformula: the g-formula approach.↩︎
Y denotes the outcome, A denotes the exposure, M denotes the mediator(s) and C denotes the exposure-outcome confounder(s), the exposure-mediator confounder(s) and the mediator-outcome confounder(s) not affected by the exposure.↩︎
continuous A is not supported when C is not empty; otherwise, it is supported.↩︎
continuous A is not supported when C is not empty; otherwise, it is supported.↩︎
count A is not supported when C is not empty; otherwise, it is supported.↩︎
count A is not supported when C is not empty; otherwise, it is supported.↩︎
closed-form parameter function estimation only supports a single mediator.↩︎
delta method inference is available only when closed-form parameter function estimation is used.↩︎
marginal effects are estimated when direct counterfactual imputation estimation is used.↩︎
conditional effects are estimated when closed-form parameter function estimation is used.↩︎
cmest
provides the option multimp = TRUE
to
perform multiple imputations for a dataset with missing values.
cmsens
conducts sensitivity analysis for unmeasured
confounding via the E-value approach by VanderWeele et
al. (2017) and Smith et
al. (2019), and sensitivity analysis for measurement error via
regression calibration by Carroll et
al. (1995) and SIMEX by Cook
et al. (1994) and Küchenhoff et
al. (2006). Sensitivity analysis for measurement error is currently
available for the regression-based approach and the
g-formula approach.
The latest version can be installed via:
::install_github("BS1125/CMAverse") devtools
Load CMAverse
:
library(CMAverse)
Valeri L, Vanderweele TJ (2013). Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods. 18(2): 137 - 150.
VanderWeele TJ, Vansteelandt S (2014). Mediation analysis with multiple mediators. Epidemiologic Methods. 2(1): 95 - 115.
Tchetgen Tchetgen EJ (2013). Inverse odds ratio-weighted estimation for causal mediation analysis. Statistics in medicine. 32: 4567 - 4580.
Nguyen QC, Osypuk TL, Schmidt NM, Glymour MM, Tchetgen Tchetgen EJ (2015). Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting. American Journal of Epidemiology. 181(5): 349 - 356.
VanderWeele TJ, Tchetgen Tchetgen EJ (2017). Mediation analysis with time varying exposures and mediators. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 79(3): 917 - 938.
Robins JM (1986). A new approach to causal inference in mortality studies with a sustained exposure period-Application to control of the healthy worker survivor effect. Mathematical Modelling. 7: 1393 - 1512.
Vansteelandt S, Bekaert M, Lange T (2012). Imputation Strategies for the Estimation of Natural Direct and Indirect Effects. Epidemiologic Methods. 1(1): 131 - 158.
Steen J, Loeys T, Moerkerke B, Vansteelandt S (2017). Medflex: an R package for flexible mediation analysis using natural effect models. Journal of Statistical Software. 76(11).
VanderWeele TJ (2014). A unification of mediation and interaction: a 4-way decomposition. Epidemiology. 25(5): 749 - 61.
Imai K, Keele L, Tingley D (2010). A general approach to causal mediation analysis. Psychological Methods. 15(4): 309 - 334.
Schomaker M, Heumann C (2018). Bootstrap inference when using multiple imputation. Statistics in Medicine. 37(14): 2252 - 2266.
VanderWeele TJ, Ding P (2017). Sensitivity analysis in observational research: introducing the E-Value. Annals of Internal Medicine. 167(4): 268 - 274.
Smith LH, VanderWeele TJ (2019). Mediational E-values: Approximate sensitivity analysis for unmeasured mediator-outcome confounding. Epidemiology. 30(6): 835 - 837.
Carrol RJ, Ruppert D, Stefanski LA, Crainiceanu C (2006). Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition. London: Chapman & Hall.
Cook JR, Stefanski LA (1994). Simulation-extrapolation estimation in parametric measurement error models. Journal of the American Statistical Association, 89(428): 1314 - 1328.
Küchenhoff H, Mwalili SM, Lesaffre E (2006). A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics. 62(1): 85 - 96.
Stefanski LA, Cook JR. Simulation-extrapolation: the measurement error jackknife (1995). Journal of the American Statistical Association. 90(432): 1247 - 56.
Valeri L, Lin X, VanderWeele TJ (2014). Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model. Statistics in medicine, 33(28): 4875–4890.
Efron B (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association. 82(397): 171-185.