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MRTAnalysis

The MRTAnalysis package provides functions to conduct post-study analyses of Micro-Randomized Trials (MRTs), focusing on estimating causal excursion effects.

• Proximal outcomes (measured shortly after each treatment decision point):
  • wcls(): Primary analysis for continuous proximal outcomes. Implements weighted and centered least squares (the \(k=1\) special case of Boruvka et al., 2018).
  • emee(): Primary analysis for binary proximal outcomes. Implements the estimator for marginal excursion effect (the \(\Delta=1\) special case of Qian et al., 2021).
  • emee2(): Variant of emee(), centering treatment in the residual term. Basis for the sample size calculator in MRTSampleSizeBinary.
• Distal outcomes (measured once at end of study):
  • dcee(): Exploratory analysis for distal causal excursion effects in MRTs (Qian et al. 2025). Supports linear models and machine-learning learners (lm, gam, random forest, ranger, SuperLearner) with optional cross-fitting.
• Mediated Effects Through Time-Varying Mediators to Distal Outcomes
  • • mcee(): Exploratory analysis for mediated causal excursion effects in MRTs, estimating natural direct excursion effects (NDEE) and natural indirect excursion effects (NIEE) through time-varying mediators. Supports GLM, GAM, random forest, ranger, and SuperLearner learners for fitting nuisance parameters.

Installation

You can install the package from CRAN:

install.packages("MRTAnalysis")

Usage

See vignettes for detailed examples:

library(MRTAnalysis)

# Proximal outcome analysis (continuous)
fit1 <- wcls(
  data = data_mimicHeartSteps,
  id = "userid", outcome = "logstep_30min",
  treatment = "intervention", rand_prob = 0.6,
  moderator_formula = ~1,
  control_formula = ~logstep_pre30min,
  availability = "avail"
)
summary(fit1)

# Distal outcome analysis
fit2 <- dcee(
  data = data_distal_continuous,
  id = "userid", outcome = "Y",
  treatment = "A", rand_prob = "prob_A",
  moderator_formula = ~1,
  control_formula = ~X,
  availability = "avail",
  control_reg_method = "lm"
)
summary(fit2)

# Mediation with distal outcome
fit3 <- mcee(
  data = data_time_varying_mediator_distal_outcome,
  id = "id", dp = "dp",
  outcome = "Y", treatment = "A", mediator = "M",
  availability = "I", rand_prob = "p_A",
  time_varying_effect_form = ~1,                # constant effects over time
  control_formula_with_mediator = ~ dp + M + X, # adjustment set
  control_reg_method = "glm"
)
summary(fit3)

References

• Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A. (2018). Assessing time-varying causal effect moderation in mobile health. Journal of the American Statistical Association, 113(523), 1112–1121. doi:10.1080/01621459.2017.1305274

• Qian, T., Yoo, H., Klasnja, P., Almirall, D., & Murphy, S. A. (2021). Estimating time-varying causal excursion effects in mobile health with binary outcomes. Biometrika, 108(3), 507–527. doi:10.1093/biomet/asaa070

• Qian, T. (2025). Distal Causal Excursion Effects: Modeling Long-Term Effects of Time-Varying Treatments in Micro-Randomized Trials. arXiv:2502.13500.

• Qian, T. (2025). Dynamic Causal Mediation Analysis for Intensive Longitudinal Data. arXiv:2506.20027.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.