A quantitative approach to the choice of number of samples for percentile estimation in bootstrap and visual predictive check analyses
Abstract Understanding the uncertainty in parameter estimates or in derived secondary variables is important in all data analysis activities. In pharmacometrics, this is often done based on the standard errors from the variance-covariance matrix of the estimates. Confidence intervals derived in this...
Saved in:
Main Authors: | E. Niclas Jonsson (Author), Joakim Nyberg (Author) |
---|---|
Format: | Book |
Published: |
Wiley,
2022-06-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques
by: Giovanni Smania, et al.
Published: (2021) -
Using forest plots to interpret covariate effects in pharmacometric models
by: E. Niclas Jonsson, et al.
Published: (2024) -
Full random effects models (FREM): A practical usage guide
by: E. Niclas Jonsson, et al.
Published: (2024) -
An introduction to the full random effects model
by: Gunnar Yngman, et al.
Published: (2022) -
Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results
by: Flynn Terry N, et al.
Published: (2004)