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LongCART (version 3.2)

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

213

Version

3.2

License

GPL (>= 2)

Maintainer

Madan Kundu

Last Published

May 17th, 2022

Functions in LongCART (3.2)

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

parameter stability test for categorical partitioning variable

Longitudinal CART with continuous response via binary partitioning

parameter stability test for continuous partitioning variable

parameter stability test for categorical partitioning variable

parameter stability test for continuous partitioning variable

KM plot for SurvCART object

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

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

Population level longitudinal profile plot for sub-groups

Survival CART with time to event response via binary partitioning

Plot an SurvCART or LongCART Object

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

succ_ia_betabinom_one

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

Predicts according to the fitted SurvCART or LongCART tree

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