PHARMACOMETRIC STATISTICS WORKSHOP
There are still some places available for this *live on-line virtual
workshop* to be held from 13^th to 20^th March 2023 (weekdays) from
13:00 to 17:00 UTC. This 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.
4) Univariate probability distributions – Expected value and variance.
5) Multivariate probability distributions – joint, marginal and
conditional distributions. The covariance matrix. Independence and
conditional independence.
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
shrinkage.
24) Asymptotic properties of the MLE, efficiency, the Cramer-Rao Lower
Bound (CRLB), 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
www.tacatraining.com <http://www.tacatraining.com>
Feedback from previous attendees is also available on our website.
Early registration is advised because the number of places is limited.
Adrian Dunne
