h_get_prediction_variance

Get prediction variance with given fit, <code>tmb_data</code> with the Monte Carlo sampling method.

internal

Mixed models for repeated measures (MMRM) are a popular
choice for analyzing longitudinal continuous outcomes in randomized
clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
<doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso
(2008) <doi:10.1177/009286150804200402> for a review. This package
implements MMRM based on the marginal linear model without random
effects using Template Model Builder ('TMB') which enables fast and
robust model fitting. Users can specify a variety of covariance
matrices, weight observations, fit models with restricted or standard
maximum likelihood inference, perform hypothesis testing with
Satterthwaite or Kenward-Roger adjustment, and extract least square
means estimates by using 'emmeans'.

Daniel Sabanes Bove

mmrm

Mixed Models for Repeated Measures

Liming Li

Julia Dedic

Doug Kelkhoff

Kevin Kunzmann

Brian Matthew Lang

Christian Stock

Ya Wang

Craig Gower-Page

Dan James

Jonathan Sidi

Daniel Leibovitz

Daniel D. Sjoberg

Lukas A. Widmer

Boehringer Ingelheim Ltd. 

Gilead Sciences, Inc. 

F. Hoffmann-La Roche AG 

Merck Sharp & Dohme, Inc. 

AstraZeneca plc 

inferential.biostatistics GmbH 

h_get_prediction_variance function

<dl><dt>object</dt>
<dd>(<code>mmrm_tmb</code>) the fitted MMRM.</dd>
<dt>nsim</dt>
<dd>(<code>count</code>) number of samples.</dd>
<dt>tmb_data</dt>
<dd>(<code>mmrm_tmb_data</code>) object.</dd></dl>

Arguments

Get Prediction Variance — h_get_prediction_variance

<dl>

<dt>object</dt>
<dd>(<code>mmrm_tmb</code>) the fitted MMRM.</dd>


<dt>nsim</dt>
<dd>(<code>count</code>) number of samples.</dd>


<dt>tmb_data</dt>
<dd>(<code>mmrm_tmb_data</code>) object.</dd>

</dl>

Get Prediction Variance

h_get_prediction_variance: Get Prediction Variance

Description

Usage

Arguments