## 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 [...]

R Package on GitHub R Package on CRAN R Package [...]

This example code calculates the minimum sample size for Sense2Stop, a 10-day, stratified micro-randomized trial. Study participants wear a chest and wrist band sensors that are used to construct a binary, time-varying stress classification at each minute of the day. The intervention is a smartphone notification to remind the participant to access a smartphone app and practice stress-reduction exercises. Intervention delivery is constrained to limit participant burden (a limit on the number of reminders sent) and to times at which the sensor-based stress classification is possible. The trial was designed to answer the questions: "Is there an effect of the reminder on near-term, proximal stress if the individual is currently experiencing stress? And, does the effect of the reminder vary with time in study?"

The Substance Abuse Research Assistance (SARA) Micro-Randomized Trial: Workflow and templates for reproducing results.

This example code describes the curation and analysis of data from a micro-randomized trial (MRT) among college students. The MRT was designed to estimate the effect of just in time digital prompts (reminders) on engagement in self-monitoring for alcohol use.

This example code describes the sample size and statistical power considerations in the design of the MARS Micro-Randomized Trial.

This web applet calculates the minimum sample size necessary for a Micro Randomized Trial with continuous proximal outcome.

- 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 Gallery
#### 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.

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