DataStorageClass


The class <code>DataStorageClass</code> is the only way the package uses to access the input data.
 The evaluated summary measures from sVar.object are stored as a matrix (<code>private$.mat.sVar</code>).
 Contains methods for replacing missing values with default in <code>gvars$misXreplace</code>.
 Also contains method for detecting / setting sVar variable type (binary, categor, contin).
 Contains methods for combining, subsetting, discretizing & binirizing summary measures <code>(sW,sA)</code>.
 For continous sVar this class provides methods for detecting / setting bin intervals,
 normalization, disretization and construction of bin indicators.
 The pointers to this class get passed on to <code>GenericModel</code> functions: <code>$fit()</code>,
 <code>$predict()</code> and <code>$predictAeqa()</code>.


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

DataStorageClass function

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

DataStorageClass: R6 class for storing, managing, subsetting and manipulating the input data.

Description

Usage

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Format

Methods

Active Bindings

Details