Demonstration Datasets for Hybrid Experimental Designs
Software Website Supporting Files About this sample [...]
MRTAnalysis: Primary and Secondary Analyses for Micro-Randomized Trial (MRT) with continuous or binary proximal outcomes
R Package on GitHub R Package on CRAN R Package [...]
Analysis of proximal and distal effects using data from a hybrid SMART-MRT in which only non-responders are micro-randomized
R Code on GitHub About this example code [...]
The Substance Abuse Research Assistant (SARA) Micro-Randomized Trial: Workflow and templates for reproducing results
The Substance Abuse Research Assistance (SARA) Micro-Randomized Trial: Workflow and templates for reproducing results.
Calculate sample size for and compare adaptive interventions within a clustered SMART with a continuous, end-of-study outcome
A clustered SMART is a type of randomized trial where intact clusters of units (e.g., patients) are randomized sequentially, yet the primary outcome is at the level of the units within the cluster. This software bundle does two things: (i) It provides data analysis code for comparing the embedded adaptive interventions in a clustered SMART on an end of study, continuous outcome. (ii) It provides the code for calculating the minimum sample size necessary for a clustered SMART in which the primary aim is the comparison of two adaptive interventions beginning with different interventions.
Estimate the proximal and distal causal effects of a just-in-time intervention component on a continuous outcome using data from a Micro Randomized Trial GalleryEstimate the proximal and distal causal effects of a just-in-time intervention component on a continuous outcome using data from a Micro Randomized Trial
Estimate the proximal and distal causal effects of a just-in-time intervention component on a continuous outcome using data from a Micro Randomized Trial
This code is used to estimate the causal effect of a just-in-time intervention component on the mean of a continuous proximal or distal outcome using data from an MRT.
Construct a more deeply-tailored adaptive intervention using data from a SMART
This code is used to investigate whether (and, if so, how) a baseline or time-varying covariate can be a useful tailoring variables in an adaptive intervention.
Compare adaptive interventions in a SMART with a binary, longitudinal outcome
This code is used to estimate and compare change in the log-odds of success in a binary longitudinal outcome for the adaptive interventions embedded in a SMART.
Compare adaptive interventions in a SMART with a continuous, longitudinal outcome (linear mixed modeling)
This code is used to estimate and compare the mean change in a longitudinal outcome for the adaptive interventions embedded in a SMART using a marginal linear mixed modeling approach.
Compare adaptive interventions in a SMART with a continuous, longitudinal outcome
This code is used to estimate and compare the mean change in a longitudinal outcome for the adaptive interventions embedded in a SMART.