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Sample size calculations for the Sense2Stop Study: A stratified, micro-randomized trial

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?"

Sample size calculations for the Sense2Stop Study: A stratified, micro-randomized trial2024-01-24T17:32:11+00:00

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.

Power calculation for the Mobile-Assistance for Regulating Smoking (MARS) Micro-Randomized Trial2024-01-24T14:49:47+00:00

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.

Calculate sample size for and compare adaptive interventions within a clustered SMART with a continuous, end-of-study outcome2024-06-07T14:34:04+00:00

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.

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 interventions2024-06-07T14:26:57+00:00

Calculate the sample size and power for a Micro-Randomized Trial with a continuous proximal outcome

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

Calculate the sample size and power for a Micro-Randomized Trial with a continuous proximal outcome2024-01-24T17:52:05+00:00

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.

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 interventions2024-06-06T22:37:12+00:00

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 binary, longitudinal outcome, in which the primary aim is to compare the log-odds of success between two adaptive interventions2024-01-24T16:12:01+00:00
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