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STAT-ACTU: cours intensif sur "Design for Clinical Trials" par Alessandra Giovagnoli

Du Mercredi 6 Mai 2009 à 10h45 au Vendredi 8 Mai 2009 à 12h45

Cours intensif par le Professeur Alessandra Giovagnoli, Université de Bologne les 6, 7 et 8 mai 2009. Le thème sera sur « Design for Clinical Trials ».

Le cours sera donné à l'UCL, Institut de statistique, voie du Roman Pays, 20 à Louvain-la-Neuve, selon l'horaire suivant :

  • 6 may, 10h45-12h45 (local: c115) : Ethics and Statistics in Clinical Trials
  • 7 may, 10h45-12h45 and 14h00-16h00 (local: c115) : Clinical trials for the comparison of treatments – The ABCD design, sequential experiments, and Adaptive design
  • 8 may, 10h45-12h45 (local: c045) : Dose escalation studies – The up & Down design

Registration is free of charge but mandatory (before May 1st) to edt-stat-actu@uclouvain.be

Note: You are also welcome on May 8 afternoon (14h30 and 16h00) to the two presentations of the seminar series of the Institute of Statistics.

Local organiser : Prof. C Legrand (catherine.legrand@uclouvain.be)


Course summary:

Ethics and statistics in clinical trials: an introduction

Clinical trials are very complex experiments with patients as subjects; this poses a critical

dilemma for investigators, known as the individual-versus-collective ethics, since it is

necessary to minimize potential harm to the patients presently under care and maximize

the experimental information. Some fundamental principles of the statistical methodology

employed in planning and analysing experiments of this type are laid out, together with a

sketch of the historical development of the subject, up to simulated clinical trials in the

present time.

Clinical trials for treatment comparison - The ABCD design, sequential experiments,

and Adaptive Designs

Randomized clinical trials, which consist in assigning treatments to subjects randomly, are

widely regarded as the most scientifically sound approach to determining which of two or

more medical treatments is better. However, for the past 15 years the attention has turned

to studying a statistical design of the trial aimed at minimizing the number of subjects

exposed to inferior treatments, increasing the patient’s chances of receiving the best one.

Needless to say, a randomization component in the assignments is always required in

order to mitigate several types of bias. Covariates too come into consideration, which are

usually random but may be known before assigning the treatment.

When the target allocation for comparing 2 treatments is balance, a restricted form of

randomization is used (Efron’s Biased Coin Design). An extension, the so-called ABCD,

which is more flexible, will be presented. However, usually the target allocation derived

from adopting a given criterion depends on the parameters of the statistical model, and as

such is unknown; this problem is known in the statistical literature as “local optimality”. A

possible solution consists in sequential experimentation, so that assignments can be

redressed towards the unknown target. Some aspects of these “adaptive” designs will be


Dose Escalation studies – The up&down design

Traditionally, dose escalation studies are binary response trials where the probability of

positive response (toxicity) is assumed to be an increasing function of the given dose level

and the aim consists in estimating the target dose at which a pre-specified probability of

positive response is associated without any parametric assumptions on the response

curve. Dose escalation studies are aimed at identifying a quantile of interest in Phase I

clinical trials. Classical examples are the median dose, usually denoted by LD50, or the

maximum tolerated dose of phase I clinical trials. Assuming that the set of available

ordered doses is fixed in advance and doses are allocated to either groups or single

patients, the “up-and-down” procedures for binary or continuous responses (U&D)

provide a possible solution for dose-finding problems

Dernière mise à jour par EDT STAT-ACTU Mercredi 29 Avril 2009