R Code for Assessing Time-Varying Causal Effect Moderation in Mobile Health Public

R code that illustrates how data from a micro-randomized trial can be analyzed to assess time-varying causal effect moderation following Boruvka et al. (2018).

README

R code for assessing time-varying causal effect moderation in mobile health

Overview

R code posted at this GitHub repository illustrates how data from a micro-randomized trial can be analyzed to assess time-varying causal effect moderation following Boruvka et al. (2018). As illustrated in the repository's script example.R, point estimates for moderated proximal and delayed treatment effects can be obtained using base R functions. If the treatment probabilities are not fixed, subjects are not always available, or the sample size is small (n < 50), variance estimates require some additional effort to obtain; the needed routines are implemented in the script xgeepack.R and an illustration of variance estimation is provided in example.R. The same illustration with use of the contributed R package geepack (H��jsgaard, Halekoh, and Yan, 2006) is provided in example_geepack.R. To calculate lagged and rolling variables, both example.R and example_geepack.R use zoo (Zeileis and Grothendieck, 2005) along with some helper functions in the repository script xzoo.R. This contributed R package is not necessary for point or variance estimation, provided that the user calculates the needed variables directly.

Downloads

  • Git Repository Snapshot (2008-11-05): master.zip

Recommended Citation

If you use this code in your own research, please cite the article listed below.

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

Additional References

H��jsgaard, S., Halekoh, U. & Yan, J. (2006) The R package geepack for generalized estimating equations, Journal of Statistical Software, 15(2):1-11.

Zeileis, A. & Grothendieck, G. (2005). zoo: S3 infrastructure for regular and irregular time series. Journal of Statistical Software, 14(6):1-27. DOI: 10.18637/jss.v014.i06