prepare.data_cached

Helper function to prepare data for fastglm

internal

Implementation of sequential trial emulation for the analysis of observational databases.
The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary
and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies,
can be used to estimate observational analogs of intention-to-treat
and per-protocol effects, and can adjust for potential selection bias
induced by losses-to-follow-up. (Paper to come).

Ryan O'Dea

SEQTaRget

Sequential Trial Emulation

Alejandro Szmulewicz

Tom Palmer

Miguel Hernán

The President and Fellows of Harvard College 

prepare.data_cached function

<dl><dt>weight</dt>
<dd>data after undergoing preparation</dd>
<dt>params</dt>
<dd>parameter from SEQuential</dd>
<dt>type</dt>
<dd>type of model, e.g. d0 = "denominator"</dd>
<dt>model</dt>
<dd>model number, e.g. d0 = "zero model"</dd>
<dt>case</dt>
<dd>case</dd>
<dt>cache</dt>
<dd>cache</dd></dl>

Arguments

Helper function to prepare data for fastglm — prepare.data_cached

<dl>

<dt>weight</dt>
<dd>data after undergoing preparation</dd>


<dt>params</dt>
<dd>parameter from SEQuential</dd>


<dt>type</dt>
<dd>type of model, e.g. d0 = "denominator"</dd>


<dt>model</dt>
<dd>model number, e.g. d0 = "zero model"</dd>


<dt>case</dt>
<dd>case</dd>


<dt>cache</dt>
<dd>cache</dd>

</dl>

prepare.data_cached: Helper function to prepare data for fastglm

Description

Usage

Arguments