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Pharmacometric Statistics Workshop

From: Adrian Dunne <adrian.dunne>
Date: Mon, 13 Sep 2021 15:55:55 +0100






Registration is now open for this live on-line virtual workshop to be held
from 14th to 21st November 2021 (weekdays) from 13:00 to 17:00 UTC. This new
workshop format has the benefits of a classroom setting with live
interaction with the instructor in order to ask questions and participate in
discussions. A further advantage is that it avoids the overhead of
international travel and all that entails.


The aim of this workshop is to give pharmacometricians a good understanding
of the statistical concepts upon which their work is based and which are of
great importance in everything they do. The emphasis will be on concepts
with an absolute minimum of mathematical details.

Attendees need only have studied statistics at foundation level prior to
taking this course.

The topics covered include;


1) Why use statistics?


2) Probability and statistical inference.


3) Laws of probability and Bayes theorem.


4) Univariate probability distributions - Expected value and variance.


5) Multivariate probability distributions - joint, marginal and conditional
distributions. The covariance matrix. Independence and conditional


6) Modelling, estimation, estimators, sampling distributions, bias,
efficiency, standard error and mean squared error.


7) Point and interval estimators. Confidence intervals.


8) Hypothesis testing, null and alternative hypotheses. P-value, Type I and
Type II errors and power.


9) Likelihood inference, maximum likelihood estimator (MLE), likelihood
ratio. BQL and censored data.


10) Invariance of the likelihood ratio and the MLE.


11) The score function, hessian, Fisher information, quadratic approximation
and standard error.


12) Wald confidence intervals and hypothesis tests.


13) Likelihood ratio tests.


14) Profile likelihood.


15) Model selection, Akaike and Bayesian Information Criteria (AIC & BIC).


16) Maximising the likelihood, Newton's method.


17) Mixed effects models.


18) Estimation of the fixed effects, conditional independence, prior and
posterior distributions.


19) Approximating the integrals, Laplace and first order (FO & FOCE)
approximations, numerical quadrature.


20) The Expectation Maximisation (EM) algorithm.


21) MU-Modelling, Iterative Two Stage (ITS)


22) Monte Carlo EM (MCEM), Importance Sampling, Direct Sampling, SAEM,
Markov Chain Monte Carlo (MCMC).


23) Estimating the random effects, empirical bayes estimates (EBE) and


24) Asymptotic properties of the MLE, efficiency, the Cramer-Rao Lower Bound
(CRLB), consistency, normality.


25) Robustness of the MLE and the Kullback-Liebler distance. The robust or
sandwich variance estimator.



For further details and to register please go to our website


Feedback from previous attendees is also available on our website.


Early registration is advised because the number of places is limited.


Adrian Dunne

Received on Mon Sep 13 2021 - 10:55:55 EDT

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