event_locf

A helper function to prepare survival data for use with LMTP estimators
by imputing outcome nodes using last outcome carried forward when an observation
experiences the event before the end-of-follow-up.

Non-parametric estimators for casual effects based on longitudinal modified treatment
policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment,
and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are
censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure
irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects
can be calculated and relative risks and odds ratios may be calculated for binary outcomes.
Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Nick Williams

lmtp

Non-Parametric Causal Effects of Feasible Interventions Based on
Modified Treatment Policies

Nicholas Williams

Iván Díaz

event_locf function

<dl><dt>data</dt>
<dd>The dataset to modify.</dd>
<dt>outcomes</dt>
<dd>A vector of outcome nodes ordered by time.</dd></dl>

Arguments

Time To Event Last Outcome Carried Forward — event_locf

<dl>

<dt>data</dt>
<dd>The dataset to modify.</dd>


<dt>outcomes</dt>
<dd>A vector of outcome nodes ordered by time.</dd>

</dl>

event_locf: Time To Event Last Outcome Carried Forward

Description

Usage

Value

Arguments

Examples