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Pharmacometric Statistics Workshop

From: Adrian Dunne <adrian.dunne>
Date: Thu, 14 Nov 2019 09:52:30 -0000






Registration is now open for this 3 day workshop to be held from 31st March
to 2nd April (inclusive) 2020 in Dublin, Ireland. (Note: earlier in the year
than previous workshops.)


The aim of this 3 day 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, 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)
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) Minimum Sufficiency, asymptotic properties of the MLE, efficiency, the
Cramer-Rao Lower Bound (CRLB), consistency, normality.


25) Robustness of the MLE and the Kullback-Liebler distance. Quasi
likelihood and 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 and
this series of workshops on Pharmacometric Statistics will come to an end in
the foreseeable future.


Adrian Dunne

Received on Thu Nov 14 2019 - 04:52:30 EST

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