simulate_ALD_pseudo_pop

Generates a synthetic cohort using a normal copula based on aggregate-level data.

internal

For the problem of indirect treatment comparison with limited subject-level data, this
package provides tools for model-based standardisation with several different computation approaches.
See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible
indirect treatment comparisons with limited individual patient data",
Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

Nathan Green

outstandR

Model-Based Standardisation for Indirect Treatment Comparison
with Limited Subject-Level Data

Chengyang Gao

Antonio Remiro-Azocar

simulate_ALD_pseudo_pop function

<dl><dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>
<dt>ipd</dt>
<dd>Individual-level patient data. Dataframe with one row per patient with outcome, treatment and covariate columns.</dd>
<dt>ald</dt>
<dd>Aggregate-level data. Long format summary statistics for each covariate and treatment outcomes.
We assume a common distribution for each treatment arm.</dd>
<dt>rho</dt>
<dd>A named square matrix of covariate correlations or single value; default NA takes from IPD.</dd>
<dt>N</dt>
<dd>Sample size for the synthetic cohort. Default is 1000.</dd>
<dt>marginal_distns</dt>
<dd>Marginal distributions names; vector default NA.
Available distributions are given in stats::Distributions. See <code>copula::Mvdc()</code> for details</dd>
<dt>marginal_params</dt>
<dd>Marginal distributions parameters;
named list of lists, default NA. See <code>copula::Mvdc()</code> for details</dd>
<dt>seed</dt>
<dd>Random seed</dd>
<dt>verbose</dt>
<dd>Default <code>FALSE</code></dd></dl>

Arguments

Simulate Aggregate-Level Data Pseudo-Population — simulate_ALD_pseudo_pop

<dl>

<dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>


<dt>ipd</dt>
<dd>Individual-level patient data. Dataframe with one row per patient with outcome, treatment and covariate columns.</dd>


<dt>ald</dt>
<dd>Aggregate-level data. Long format summary statistics for each covariate and treatment outcomes.
We assume a common distribution for each treatment arm.</dd>


<dt>rho</dt>
<dd>A named square matrix of covariate correlations or single value; default NA takes from IPD.</dd>


<dt>N</dt>
<dd>Sample size for the synthetic cohort. Default is 1000.</dd>


<dt>marginal_distns</dt>
<dd>Marginal distributions names; vector default NA.
Available distributions are given in stats::Distributions. See <code>copula::Mvdc()</code> for details</dd>


<dt>marginal_params</dt>
<dd>Marginal distributions parameters;
named list of lists, default NA. See <code>copula::Mvdc()</code> for details</dd>


<dt>seed</dt>
<dd>Random seed</dd>


<dt>verbose</dt>
<dd>Default <code>FALSE</code></dd>

</dl>

simulate_ALD_pseudo_pop: Simulate Aggregate-Level Data Pseudo-Population

Description

Usage

Value

Arguments