est_g_cens

A <code>numeric</code> vector of loss to follow-up indicators.

C_cens

A <code>numeric</code> vector of observed exposure values.

A <code>numeric</code> matrix of observed baseline covariate values.

A <code>numeric</code> vector of observation-level sampling
weights, as produced by the internal procedure to estimate the two-phase
sampling mechanism <code><a rd-options="" href="/link/est_samp?package=txshift&version=0.3.8" data-mini-rdoc="txshift::est_samp">est_samp</a></code>.

samp_weights

A <code>character</code> indicating whether to use GLMs or Super
Learner to fit the censoring mechanism. If option "glm" is selected, the
argument <code>glm_formula</code> must NOT be <code>NULL</code>, instead containing a
model formula (as per <code><a rd-options="stats" href="/link/glm?package=txshift&version=0.3.8&to=stats" data-mini-rdoc="stats::glm">glm</a></code>) as a <code>character</code>. If
the option "sl" is selected, the argument <code>sl_learners</code> must NOT be
<code>NULL</code>; instead, an instantiated sl3 <code>Lrnr_sl</code> object,
specifying learners and a metalearner for the Super Learner fit, must be
provided. Consult the documentation of sl3 for details.

fit_type

A <code>character</code> giving a <code><a rd-options="stats" href="/link/formula?package=txshift&version=0.3.8&to=stats" data-mini-rdoc="stats::formula">formula</a></code>
for fitting a (generalized) linear model via <code><a rd-options="stats" href="/link/glm?package=txshift&version=0.3.8&to=stats" data-mini-rdoc="stats::glm">glm</a></code>.

glm_formula

Object containing a set of instantiated learners from the
sl3, to be used in fitting an ensemble model.

sl_learners

Efficient estimation of the population-level causal effects of
stochastic interventions on a continuous-valued exposure. Both one-step and
targeted minimum loss estimators are implemented for the counterfactual mean
value of an outcome of interest under an additive modified treatment policy,
a stochastic intervention that may depend on the natural value of the
exposure. To accommodate settings with outcome-dependent two-phase
sampling, procedures incorporating inverse probability of censoring
weighting are provided to facilitate the construction of inefficient and
efficient one-step and targeted minimum loss estimators. The causal
parameter and its estimation were first described by D<c3><ad>az and van der Laan
(2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
estimation procedure and its application to data from two-phase sampling
designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
implementation is described in NS Hejazi and DC Benkeser (2020)
<doi:10.21105/joss.02447>. Estimation of nuisance parameters may be
enhanced through the Super Learner ensemble model in 'sl3', available for
download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Nima Hejazi

txshift

Efficient Estimation of the Causal Effects of Stochastic
Interventions

David Benkeser

Iv<c3><a1>n D<c3><ad>az

Jeremy Coyle

Mark van der Laan

est_g_cens function

A <code>numeric</code> vector of observation-level sampling
weights, as produced by the internal procedure to estimate the two-phase
sampling mechanism <code><a rd-options='' href='est_samp'>est_samp</a></code>.

A <code>character</code> indicating whether to use GLMs or Super
Learner to fit the censoring mechanism. If option "glm" is selected, the
argument <code>glm_formula</code> must NOT be <code>NULL</code>, instead containing a
model formula (as per <code><a rd-options='stats' href='glm'>glm</a></code>) as a <code>character</code>. If
the option "sl" is selected, the argument <code>sl_learners</code> must NOT be
<code>NULL</code>; instead, an instantiated sl3 <code>Lrnr_sl</code> object,
specifying learners and a metalearner for the Super Learner fit, must be
provided. Consult the documentation of sl3 for details.

A <code>character</code> giving a <code><a rd-options='stats' href='formula'>formula</a></code>
for fitting a (generalized) linear model via <code><a rd-options='stats' href='glm'>glm</a></code>.

est_g_cens: Estimate the Censoring Mechanism

Description

Usage

Arguments

Value

Details