ContinModel


This R6 class defines and fits a conditional probability model <code>P(A[j]|W,...)</code> for a univariate
 continuous summary measure <code>A[j]</code>. This class inherits from <code><a rd-options="" href="/link/GenericModel?package=stremr&version=0.4" data-mini-rdoc="stremr::GenericModel">GenericModel</a></code> class.
 Defines the fitting algorithm for a regression model <code>A[j] ~ W + ...</code>.
 Reconstructs the likelihood <code>P(A[j]=a[j]|W,...)</code> afterwards.
 Continuous <code>A[j]</code> is discretized using either of the 3 interval cutoff methods,
 defined via <code><a rd-options="" href="/link/RegressionClass?package=stremr&version=0.4" data-mini-rdoc="stremr::RegressionClass">RegressionClass</a></code> object <code>reg</code> passed to this class constructor.
 The fitting algorithm estimates the binary regressions for hazard <code>Bin_A[j][i] ~ W</code>,
 i.e., the probability that continuous <code>A[j]</code> falls into bin <code>i</code>, <code>Bin_A[j]_i</code>,
 given that <code>A[j]</code> does not belong to any prior bins <code>Bin_A[j]_1, ..., Bin_A[j]_{i-1}</code>.
 The dataset of discretized summary measures (<code>BinA[j]_1,...,BinA[j]_M</code>) is created
 inside the passed <code>data</code> or <code>newdata</code> object while discretizing <code>A[j]</code> into <code>M</code> bins.


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

ContinModel function

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

Format

Methods

Active Bindings


This R6 class defines and fits a conditional probability model <code>P(A[j]|W,...)</code> for a univariate
 continuous summary measure <code>A[j]</code>. This class inherits from <code><a rd-options='' href='GenericModel'>GenericModel</a></code> class.
 Defines the fitting algorithm for a regression model <code>A[j] ~ W + ...</code>.
 Reconstructs the likelihood <code>P(A[j]=a[j]|W,...)</code> afterwards.
 Continuous <code>A[j]</code> is discretized using either of the 3 interval cutoff methods,
 defined via <code><a rd-options='' href='RegressionClass'>RegressionClass</a></code> object <code>reg</code> passed to this class constructor.
 The fitting algorithm estimates the binary regressions for hazard <code>Bin_A[j][i] ~ W</code>,
 i.e., the probability that continuous <code>A[j]</code> falls into bin <code>i</code>, <code>Bin_A[j]_i</code>,
 given that <code>A[j]</code> does not belong to any prior bins <code>Bin_A[j]_1, ..., Bin_A[j]_{i-1}</code>.
 The dataset of discretized summary measures (<code>BinA[j]_1,...,BinA[j]_M</code>) is created
 inside the passed <code>data</code> or <code>newdata</code> object while discretizing <code>A[j]</code> into <code>M</code> bins.


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

ContinModel: R6 class for fitting and predicting joint probability for a univariate continuous summary A[j]

Description

Usage

Arguments

Format

Methods

Active Bindings

Details