Table 2.
Features of the causal mediation software packages
| Outcome model | Mediator model | Treatment variable | Standard errors/ Confidence intervals | Missing Data | Sensitivity Analysis | Multiple Mediators | Latent variables | Moderated mediation | |
|---|---|---|---|---|---|---|---|---|---|
| VVW | Linear, logistic, Poisson, negative binomial, Cox, or accelerated failure time (exponential or Weibull).a | Linear or logistic. | Binary or continuous. | Delta method or percentile bootstrap. | Complete case analysis only. | No | No | No | No |
| PARAMED | Linear, logistic, Poisson, negative binomial. | linear or logistic. | Binary or continuous. | Delta method, bias-corrected bootstrap. | Complete case analysis only. | No | No | No | No |
| CAUSALMED 9.4 TS1M6 | Linear, logistic, Poisson, negative binomial. | linear or logistic. | Binary or continuous. | Delta method, normal-based bootstrap, percentile bootstrap, or bias-corrected bootstrap. | Complete case analysis only. | No | No | No | No |
| Med4Way | Linear, logistic, Poisson, negative binomial, log-binomial, Cox, or accelerated failure time (exponential or Weibull). | linear or logistic. | Binary or continuous. | Delta method, normal-based bootstrap, percentile bootstrap, bias-corrected bootstrap, or bias-corrected and accelerated bootstrap. | Complete case analysis only. | No | No | No | No |
| Model Indirect (Mplus 8.4) | Linear, (ordered) logistic, (ordered) probit, censored, Poisson, or negative binomial.b | Linear, (ordered) logistic, (ordered) probit, or censored.b | Binary or continuous. | Delta method, percentile bootstrap, bias-corrected bootstrap. Robust standard errors, Bayesian credible intervals. | Multiple imputation, maximum likelihood, or complete case analysis. | Yes | Yes, only overall indirect effect estimates are provided assuming no interactions between mediators. | Yes | Yes |
| Mediation (version 4.5) | Linear, (ordered) logistic, (ordered) probit, Poisson, Cox, accelerated failure time, tobit, quantile regression, generalized additive model, or (generalized) mixed-effects.c | Linear, (ordered) logistic, (ordered) probit, Poisson, Cox, accelerated failure time, quantile regression, generalized additive model, or (generalized) mixed-effects.c | Binary, continuous, or multicategorical. | Quasi-Bayesian Monte Carlo, percentile bootstrap, or bias-corrected and accelerated bootstrap. Robust standard errors. | Complete case analysis or multiple imputation for the input models using standard multiple imputation functions | Yes | Yes, overall indirect effect estimates are provided assuming no interactions between mediators. | No | Yes |
| Medflex (version 0.6–6) | Linear, logistic, probit, or Poisson.d |
Imputation-based approach: linear, logistic, probit, Poisson, multinomial logistic, ordinal logistic.d Weighting-based approach: linear, logistic, probit, Poisson, multinomial logistic.d |
Binary, continuous, or multicategorical. | Normal-based bootstrap, percentile bootstrap, bias-corrected bootstrap, or bias-corrected and accelerated bootstrap. Robust standard errors. | Default for weighting-based approach is complete case analysis. Imputation-based approach uses imputation of missing values conditional on treatment, mediator, and baseline covariates. Multiple imputation is supported for both approaches. | No | Yes, overall indirect effect estimates are provided. | No | Yes |
Cox and accelerated failure time models are only facilitated in the SAS macro (Valeri & VanderWeele, 2015).
A two-part outcome model can be estimated using a combination of the Model Indirect command and the Model Constraint command. A multinomial mediator can be assumed using the Model Constraint command. Mplus uses the latent variable response formulation for categorical dependent variables.
The quasi-Bayesian approximation cannot be used if the mediator model is specified as a quantile regression model or a generalized additive model or if the outcome model is specified as a generalized additive model or an ordered logistic (probit) model. See Tingley et al., 2014 for additional information and the specific R packages that can be used to fit the mediator and outcome models.
See Steen et al., 2017 for additional information and the specific R packages that can be used to fit the mediator and outcome models.