MRTAnalysis: Primary and Secondary Analyses for Micro-Randomized Trial (MRT) with continuous or binary proximal outcomes
About This Code
This code is an R package for primary and secondary analyses for MRT with continuous or binary proximal outcomes
How can a behavioral scientist use this code?
Behavioral intervention scientists can use this R package to conduct primary and secondary analyses for micro-randomized trial (MRT). The package applies to settings where the proximal outcome is either continuous or binary, and where the treatment level is binary (treatment vs. no treatment). In particular, they can use it to assess marginal causal excursion effects and moderated causal excursion effects.
What method does this code implement?
The method implemented for MRT with continuous outcomes is the weighted centered least squares (WCLS) by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274>. The method implemented for MRT with binary outcomes is the estimator for marginal excursion effect (EMEE) by Qian et al. (2021) <doi:10.1093/biomet/asaa070>. The R package currently only supports estimation of immediate effects; estimation of delayed effects are not implemented yet.
1. Qian, Tianchen, Ashley E. Walton, Linda M. Collins, Predrag Klasnja, Stephanie T. Lanza, Inbal Nahum-Shani, Mashfiqui Rabbi et al. “The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations.” Psychological methods (2022).
A review paper for MRT which illustrates the primary analysis methods for MRT with continuous proximal outcome.
2. Boruvka, Audrey, Daniel Almirall, Katie Witkiewitz, and Susan A. Murphy. “Assessing time-varying causal effect moderation in mobile health.” Journal of the American Statistical Association 113, no. 523 (2018): 1112-1121.
The original paper that describes the WCLS method for continuous outcome.
3. Qian, Tianchen, Hyesun Yoo, Predrag Klasnja, Daniel Almirall, and Susan A. Murphy. “Estimating time-varying causal excursion effects in mobile health with binary outcomes.” Biometrika 108, no. 3 (2021): 507-527.
The original paper that describes the EMEE method for binary outcome.
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