gastricbypassW

Data from the gastric bypass study
where the bodyweight and serum glucagon (a gut hormone) were measured in 20 obese subjects prior and after gastric bypass surgery.
This dataset is in the wide format (i.e. one line per patient).
<ul>
<li><code>id</code>: patient identifier.</li>
<li><code>weight1</code>: bodyweight (in kg) 3 months before surgery.</li>
<li><code>weight2</code>: bodyweight (in kg) 1 week before surgery.</li>
<li><code>weight3</code>: bodyweight (in kg) 1 week after surgery.</li>
<li><code>weight4</code>: bodyweight (in kg) 3 months after surgery.</li>
<li><code>glucagonAUC1</code>: glucagon (in pmol/l x hours) 3 months before surgery.</li>
<li><code>glucagonAUC2</code>: glucagon (in pmol/l x hours) 1 week before surgery.</li>
<li><code>glucagonAUC3</code>: glucagon (in pmol/l x hours) 1 week after surgery.</li>
<li><code>glucagonAUC4</code>: glucagon (in pmol/l x hours) 3 months after surgery.</li>
</ul>

datasets

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
taught by the section of Biostatistics of the University of Copenhagen.
It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
Normalized residuals are provided to assess model misspecification.
Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
Predictions can be computed conditional to covariates only or also to outcome values.

gastricbypassW: Data From The Gastric Bypass Study (Wide Format)

Description

Usage

Arguments

References