predict_traj

Function to predict trajectory groups for deterministic treatment regimes
used with gformula and pooled LTMLE.

Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.

Awa Diop

trajmsm

Marginal Structural Models with Latent Class Growth Analysis of
Treatment Trajectories

Denis Talbot

predict_traj function

<dl><dt>identifier</dt>
<dd>Name of the column of the unique identifier.</dd>
<dt>total_followup</dt>
<dd>Number of measuring times.</dd>
<dt>treatment</dt>
<dd>Name of the time-varying treatment.</dd>
<dt>time</dt>
<dd>Name of the variable time.</dd>
<dt>time_values</dt>
<dd>Values of the time variable.</dd>
<dt>trajmodel</dt>
<dd>Trajectory model built with the observed treatment.</dd></dl>

Arguments

Author

Predict trajectory groups for deterministic treatment regimes — predict_traj

<dl>

<dt>identifier</dt>
<dd>Name of the column of the unique identifier.</dd>


<dt>total_followup</dt>
<dd>Number of measuring times.</dd>


<dt>treatment</dt>
<dd>Name of the time-varying treatment.</dd>


<dt>time</dt>
<dd>Name of the variable time.</dd>


<dt>time_values</dt>
<dd>Values of the time variable.</dd>


<dt>trajmodel</dt>
<dd>Trajectory model built with the observed treatment.</dd>

</dl>

predict_traj: Predict trajectory groups for deterministic treatment regimes

Description

Usage

Value

Arguments

Author