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.

Multiphase Optimization Strategy (MOST) – Code to design a factorial experiment2022-10-06T19:38:55+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 interventions2022-10-06T19:39:04+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 outcome2022-10-06T19:39:19+00:00

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.

Estimate the proximal and distal causal effects of a just-in-time intervention component on a continuous outcome using data from a Micro Randomized Trial2022-10-06T19:41:10+00:00

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.

Construct a more deeply-tailored adaptive intervention using data from a SMART2022-10-06T19:41:42+00:00
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