BinomialGLM


This R6 class can request, store and manage the design matrix Xmat, as well as the binary outcome Bin for the
 logistic regression P(Bin|Xmat).
 Can also be used for converting data in wide format to long when requested,
 e.g., when pooling across binary indicators (fitting one pooled logistic regression model for several indicators)
 The class has methods that perform queries to data storage R6 class DataStorageClass to get appropriate data columns & row subsets


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

BinomialGLM function

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

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An <code><a rd-options='' href='R6Class'>R6Class</a></code> generator object

BinomialGLM: R6 class for storing the design matrix and the binary outcome for a single GLM (logistic) regression

Description

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Details