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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 outcome2024-01-24T17:24:46+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 interventions2024-01-24T17:21:45+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 interventions2024-01-24T17:18:27+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

Calculate the sample size for a SMART with a continuous, longitudinal outcome, in which the primary aim is to compare the mean between two adaptive interventions

This code calculates the minimum sample size necessary for a SMART with a continuous longitudinal outcome, in which the primary aim is a comparison of the mean of the outcome between two adaptive interventions starting with different interventions.

Calculate the sample size for a SMART with a continuous, longitudinal outcome, in which the primary aim is to compare the mean between two adaptive interventions2024-01-24T16:14:27+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 SMART2024-01-24T15:02:00+00:00

Compare adaptive interventions in a SMART with a binary, longitudinal outcome

This code is used to estimate and compare change in the log-odds of success in a binary longitudinal outcome for the adaptive interventions embedded in a SMART.

Compare adaptive interventions in a SMART with a binary, longitudinal outcome2024-01-24T14:58:59+00:00

Compare adaptive interventions in a SMART with a continuous, longitudinal outcome (linear mixed modeling)

This code is used to estimate and compare the mean change in a longitudinal outcome for the adaptive interventions embedded in a SMART using a marginal linear mixed modeling approach.

Compare adaptive interventions in a SMART with a continuous, longitudinal outcome (linear mixed modeling)2024-01-24T15:04:18+00:00

Compare adaptive interventions in a SMART with a continuous, longitudinal outcome

This code is used to estimate and compare the mean change in a longitudinal outcome for the adaptive interventions embedded in a SMART.

Compare adaptive interventions in a SMART with a continuous, longitudinal outcome2024-01-24T15:06:15+00:00
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