QlearnModel


R6 class for controlling the internal implementation of Q-learning functionality.
 Supports sequential (recursive) G-computation and longitudinal TMLE.
 Inherits from <code>BinaryOutcomeModel</code> R6 Class.


class

Analysis of longitudinal time-to-event or time-to-failure data.
Estimates the counterfactual discrete survival curve under static, dynamic and
stochastic interventions on treatment (exposure) and monitoring events over time.
Estimators (IPW, MSM-IPW, GCOMP, longitudinal TMLE) adjust for measured time-varying
confounding and informative right-censoring. Model fitting can be performed either
with GLM or H2O-3 machine learning libraries.
The exposure, monitoring and censoring variables can be coded as either binary,
categorical or continuous. Each can be multivariate (e.g., can use more than one
column of dummy indicators for different censoring events).
The input data needs to be in long format.

Oleg Sofrygin

stremr

Streamlined Estimation of Survival for Static, Dynamic and
Stochastic Treatment and Monitoring Regimes

QlearnModel function

An <code><a rd-options="" href="/link/R6Class?package=stremr&version=0.4" data-mini-rdoc="stremr::R6Class">R6Class</a></code> generator object

QlearnModel: R6 Class for Q-Learning

Description

Usage

Arguments

Format

Methods

Active Bindings

Details