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

About This Example Code

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?”

How can a behavioral scientist use this code?

Behavioral intervention scientists can use this code as a starting point for calculating the sample size and power for micro-randomized trials that are similar to the Sense2Stop study.

What method does this code implement?

The code is based on a novel method that (i) balances small sample bias and power, (ii) avoids causal bias, and (iii) informs the expected number of episodes during the remaining part of the day that will be classified as stressed versus not stressed using a generative model (that is based on prior observational study data). Note that the code was developed to be specific to the design of the Sense2Stop study. Significant modification may potentially be needed should this code be used as a starting point to calculate sample size in studies whose design have significant departures from the Sense2Stop study design.

Access Links

View Repository on GitHub

LET’S STAY IN TOUCH

Join the d3center Mailing List

Keep up to date with the latest news, events, software releases, learning modules, and resources from the d3center.