## Power calculation for the Mobile-Assistance for Regulating Smoking (MARS) Micro-Randomized Trial

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

d3center - Data Science For Dynamic Intervention Decision-Making Center

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

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.

- Calculate the sample size for a SMART with a longitudinal count outcome, in which the primary aim is to compare the mean count between two adaptive interventions Gallery
#### Calculate the sample size for a SMART with a longitudinal count outcome, in which the primary aim is to compare the mean count between two adaptive interventions

This code calculates the minimum sample size necessary for a SMART with a longitudinal, count outcome, in which the primary aim is a comparison of the mean count between two adaptive interventions starting with different interventions.

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

- Calculate the sample size for a SMART with a binary or continuous end of study outcome, in which the primary aim is to compare the mean of the outcome between two adaptive interventions Gallery
#### Calculate the sample size for a SMART with a binary or continuous end of study outcome, in which the primary aim is to compare the mean of the outcome between two adaptive interventions

This web applet calculates the minimum sample size necessary for a SMART with an end of study binary or continuous outcome, in which the primary aim is a comparison of any two adaptive interventions starting with different interventions.

This web applet calculates the minimum sample size to ensure that a sufficient number of participants are assigned to all treatment sequences in a SMART.

- Calculate the sample size for a SMART with a binary, longitudinal outcome, in which the primary aim is to compare the log-odds of success between two adaptive interventions Gallery
#### Calculate the sample size for a SMART with a binary, longitudinal outcome, in which the primary aim is to compare the log-odds of success between two adaptive interventions

This code calculates the minimum sample size necessary for a SMART with a binary longitudinal outcome, in which the primary aim is a comparison of the log-odds of sucess between two adaptive interventions starting with different interventions.

- Calculate the sample size for a SMART with a continuous, longitudinal outcome, in which the primary aim is to compare the mean between two adaptive interventions Gallery
#### Calculate the sample size for a SMART with a continuous, longitudinal outcome, in which the primary aim is to compare the mean between two adaptive interventions

This code calculates the minimum sample size necessary for a SMART with a continuous longitudinal outcome, in which the primary aim is a comparison of the mean of the outcome between two adaptive interventions starting with different interventions.

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