MRTAnalysis: Primary and Secondary Analyses for Micro-Randomized Trial (MRT) with continuous or binary proximal outcomes
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Calculate the sample size or power for a Micro-Randomized Trial (MRT) with a binary proximal outcome
Multiphase Optimization Strategy (MOST) – Code to design a factorial experiment
Contains three functions intended to be helpful in the context of MOST. FactorialPowerPlan calculates sample size needed for factorial experiments under various assumptions. RandomAssignmentGenerator generates a list of random numbers for use as condition numbers to which to assign individuals in a factorial experiment. RelativeCosts1 implements a partly customizable form of a figure in the paper "Design of Experiments with Multiple Independent Variables: A Resource Management Perspective on Complete and Reduced Factorial Designs" (Collins et al., 2009) illustrating the efficiency of factorial experiments.
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 GalleryCalculate 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
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 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.
- 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
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.
Construct a more deeply-tailored adaptive intervention using data from a SMART
This code is used to investigate whether (and, if so, how) a baseline or time-varying covariate can be a useful tailoring variables in an adaptive intervention.