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LongCART (version 3.1)

Recursive Partitioning for Longitudinal Data and Right Censored Data Using Baseline Covariates

Description

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

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Version

Install

install.packages('LongCART')

Monthly Downloads

208

Version

3.1

License

GPL (>= 2)

Maintainer

Madan Kundu

Last Published

May 31st, 2021

Functions in LongCART (3.1)

Longitudinal CART with continuous response via binary partitioning

Converted AIDS Clinical Trials Group Study 175 (source: speff2trial package)

parameter stability test for categorical partitioning variable

parameter stability test for continuous partitioning variable

Probability of trial ans clinical success for a prospective trial using normal-normal approximation

parameter stability test for continuous partitioning variable

German Breast Cancer Study Group 2 (source: TH.data package)

KMPlot.SurvCART

KM plot for SurvCART object

parameter stability test for categorical partitioning variable

ProfilePlot.LongCART

Population level longitudinal profile plot for sub-groups

Conditional power and predictive power of success based on interim results using normal-normal approximation

Place text on SurvCART or LongCART tree

succ_ia_betabinom_two

Determines predictive power of success based on interim results and beta priors for two-sample binary data

Survival CART with time to event response via binary partitioning

Plot an SurvCART or LongCART Object

succ_ia_betabinom_one

Determines predictive power of success based on interim results and beta prior for one-sample binary data