# [NMusers] Pharmacometric Statistics Workshop

Date: Wed, 1 Aug 2018 18:21:39 +0100

There are still a few places available for the following workshop.

PHARMACOMETRIC STATISTICS

to be held from

16th to 18th October 2018 in Dublin, Ireland.

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 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) Minimal sufficiency and 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, nested models.

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)

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, the Kullback-Liebler distance. Quasi

likelihood and the robust or sandwich variance estimator.

For further details and to register please go to our website

<http://www.tacatraining.com> www.tacatraining.com