create_probMatrix

create_probMatrix creates a variable that contains the transition probabilities ("initial", "forward", "backward", "intermittent", "observed")

Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.

Pavlos Mamouris

ImputeLongiCovs

Longitudinal Imputation of Categorical Variables via a Joint
Transition Model

Vahid Nassiri

Geert Molenberghs

Geert Verbeke

create_probMatrix function

<dl><dt>input_data</dt>
<dd>A dataset in a format similar to `initial_data`. This dataset must contain the variables "state_from", which is the status at the beginning of the transition (say smoker in 2010),
"state_to", which is the status at the end of the transition (say ex-smoker in 2011) and "tran_Year", which is an integer variable that is equal
to the number of transitions. "tran_Year" == 1 means that the transition occurs from 2010 to 2011, "tran_Year" == 2, from 2011 to 2012, up to
the total number of transitions</dd>
<dt>patient_id</dt>
<dd>A character variable that specifies the column name with the unique Id of the patient</dd></dl>

Arguments

create_probMatrix — create_probMatrix

<dl>

<dt>input_data</dt>
<dd>A dataset in a format similar to `initial_data`. This dataset must contain the variables "state_from", which is the status at the beginning of the transition (say smoker in 2010),
"state_to", which is the status at the end of the transition (say ex-smoker in 2011) and "tran_Year", which is an integer variable that is equal
to the number of transitions. "tran_Year" == 1 means that the transition occurs from 2010 to 2011, "tran_Year" == 2, from 2011 to 2012, up to
the total number of transitions</dd>


<dt>patient_id</dt>
<dd>A character variable that specifies the column name with the unique Id of the patient</dd>

</dl>

create_probMatrix: create_probMatrix

Description

Usage

Value

Arguments

Examples