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Taca Training Pharmacometric Statistics Workshop

From: adrian.dunne
Date: Tue, 20 Jan 2015 17:04:45 -0000





Registration is now open for this 3 day workshop to be held from 29th
September to 1st October 2015 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


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)
approximations, numerical quadrature.


20) The Expectation Maximisation (EM) algorithm.


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


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


23) Robustness of the MLE, the Kullback-Liebler distance. Quasi likelihood
and the robust or sandwich variance estimator.


24) Time to event (survival) analysis. Survivor and Hazard functions.


25) Kaplan-Meier plots. Log-rank and Wilcoxon tests.


26) Parametric and semi-parametric proportional hazards models.


27) Partial and full likelihood inference.


For further details and to register please go to our website


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


Thank you very much for your attention.


Adrian Dunne



Received on Tue Jan 20 2015 - 12:04:45 EST

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