split_data

Function to split the data into multiple subsets of size s each
one subset corresponding to one time-interval.

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

split_data function

<dl><dt>obsdata</dt>
<dd>Observed data in wide format.</dd>
<dt>total_followup</dt>
<dd>Total length of follow-up.</dd>
<dt>ntimes_interval</dt>
<dd>Number of measuring times per interval.</dd>
<dt>time</dt>
<dd>Name of the time variable.</dd>
<dt>time_values</dt>
<dd>Measuring times.</dd>
<dt>identifier</dt>
<dd>Identifier of individuals.</dd></dl>

Arguments

Author

Split observed data into multiple subsets — split_data

<dl>

<dt>obsdata</dt>
<dd>Observed data in wide format.</dd>


<dt>total_followup</dt>
<dd>Total length of follow-up.</dd>


<dt>ntimes_interval</dt>
<dd>Number of measuring times per interval.</dd>


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


<dt>time_values</dt>
<dd>Measuring times.</dd>


<dt>identifier</dt>
<dd>Identifier of individuals.</dd>

</dl>

split_data: Split observed data into multiple subsets

Description

Usage

Value

Arguments

Author

Examples