SubgroupBoost.wd: Title Win-difference based value function guided subgrouping

In liupeng2117/SubgroupBoost: Value function Guided Subgrouping via Gradient Tree Boosting

Description

Title Win-difference based value function guided subgrouping

Usage

SubgroupBoost.wd(dat, info, comparison)

Arguments

dat	a dataframe, rows are subject and columns are predictors, (censored) or 1 (event).
info	a dataframe of treatment indicator variable trt01p, event indicator variable evnt, and time variable aval. trt01p is coded as -1 (control) or 1 (treatment),and evnt is coded as 0 (censored) or 1(event). If there are two survival outcomes, use evnt1,evnt2 and aval1,aval2 to distinguish the two outcomes.
comparison	The types of outcome to compare in win-difference based value function. The possible choices are survival survivalTwo time-to-event outcomes survivalone time-to-event outcome continousone continous outcome, larger value means more benefit from treatment binaryone binary outcome, larger value means more benefit from treatment ordinalone ordinal discrete outcome, larger value means more benefit from treatment

Details

add details later

Value

a fitted gradient boosting model

Examples

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liupeng2117/SubgroupBoost documentation built on Feb. 7, 2022, 1 p.m.