Free Software

An important aspect of our research is ensuring that the methods we develop are placed directly into the hands of the bio-behavioral, education and quantitative intervention scientists who will use them. To accomplish this, our strategy includes the development and dissemination of free software.

SAS code to compare embedded adaptive interventions

Software for informing the development of an Adaptive Intervention (Q-Learning)

Data analysis method for using repeated continuous outcome measures from SMART studies to compare adaptive interventions.

Linear mixed models for comparing adaptive interventions on a longitudinal outcome from SMART studies.

Data analysis method for using longitudinal binary outcome data from SMART studies to compare adaptive interventions.

Sample size calculation for comparing adaptive interventions in SMARTs with a binary longitudinal outcome

Applet for calculating the minimum sample size for a pilot SMART

Applet for calculating the minimum sample size for SMARTs with Binary or Continuous Outcomes

Sample size calculation for comparing adaptive interventions in SMARTs with a continuous longitudinal outcome.

Sample size calculation and data analysis for comparing cluster-level adaptive interventions using data from SMART studies.

Planning SMARTs: Sample size estimation for comparing dynamic treatment regimens using longitudinal count outcomes with overdispersion.

Power Calculation for the Mobile-Assistance for Regulating Smoking (MARS) Micro-Randomized Trial.

A sample size calculator for micro-randomized trials.

R code for assessing time-varying causal effect moderation for analyzing data from an MRT.

Data analysis method for assessing time-varying causal effect moderation in mobile health.

The Substance Abuse Research Assistance (SARA) MRT: Workflow and templates for reproducing results.

Engagement in a Digital World: Examples of Common Engagement Strategies

Self-Relevant Appeals to Engage in Self-Monitoring of Alcohol Use: A Micro-randomized Trial

Simulating and analyzing data from a Hybrid Experimental Design (HED)

Multiphase Optimization Strategy (MOST) R Package and Functions