Queen Mary and Westfield College
School of Mathematical Sciences

Autumn 1998

STATISTICS SEMINARS : DESIGN OF EXPERIMENTS

All are welcome

The talks are held at 1630 hours in the Mathematics Seminar Room (103) on
Level 1 of the Mathematics Building, Queen Mary and Westfield College.
Tea and coffee are available in the Mathematics Common Room (102) from
1500 hours.

The nearest underground station is Stepney Green.  Turn left at the exit
and walk 400 yards.

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Date		Speaker	Title						University

08 Oct. 98	Karen Ayres	"Measuring Genetic Correlations 		Reading
				  Within and Between Loci".

29 Oct.	Neil Butler	"A Comparison of Optimality			Reading
				  Criteria for Quadratic Response			
				  Surface and Spline Smoothing Models".

19 Nov.	Deborah Ashby	"Casting a Sceptical Eye over the		QMW.
  				 Data : Implementing Bayesian Data	
  				 Monitoring in Cancer Clinical Trials".

17 Dec.	Ben Torsney	"Results on D-Optimal Designs for		Glasgow
				  Weighted Regression and Binary
				  Regression Models".
____________________________________________________________________________
______
For more information ask:

Barbara Bogacka
School of Mathematical Sciences
Queen Mary and Westfield College
Mile End Road, London E1 4NS

Tel: 	0171 975 5497
e-mail: [log in to unmask]
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The seminar information is kept on:

http://www.maths.qmw.ac.uk/~rab/seminars.html					

Please see attached Abstracts and Important Notice

										



ABSTRACTS


"Measuring Genetic Correlations Within and Between Loci"

Karen Ayres

Forensic DNA match probabilities often assume independence of
genes within and between loci. It is therefore important to
investigate these assumptions of independence, and one approach
is hypothesis testing.  However, more information is available
via posterior probability density curves of parameters measuring
dependence. Useful parameters include the inbreeding coefficient,
which measures dependence within loci, and gametic disequilibrium
coefficients, which measure the dependence of genes at different
loci within gametes (sex cells).

Adopting a Bayesian approach, Markov chain Monte Carlo methods
are described for sampling from the posterior distributions of
these parameters. The methods are demonstrated via application
to simulated data, and also to population data from New Zealand.
The implications for DNA match probabilities are briefly discussed.

oooOOOooo



"A Comparison of Optimality Criteria for Quadratic Response Surface
and Spline Smoothing Models"

Neil Butler

This talk will consist of two parts. The first part introduces some
optimality criteria for quadratic response surface designs based on
the gradient of the fitted response. These are then compared with
more established optimality criteria. The second part considers optimal
designs for smoothing spline and linear variance models, which are
shown to provide a link between optimal design theory and sampling
methods on an interval.



oooOOOooo






								

ABSTRACTS CONTINUED


"Casting a Sceptical Eye over the Data: Implementing Bayesian
Data Monitoring in Cancer Clinical Trials"

Deborah Ashby

Many clinical trials organisations use regular interim analyses
to monitor the accruing results in large clinical trials. Classical
rules, such as the group-sequential procedures of Peto, O'Brien
and Fleming or Pocock have traditionally been based on P-values.
However, none of these rules formally assess the impact that the
results of a clinical trial might have on clinical practice. Thus
a trial might be terminated early because of apparent treatment
benefit, but fail to influence future clinicians to modify their
future treatment policy. Bayesian rules have been proposed that
have the potential to overcome these difficulties (Freedman,
Spiegelhalter and Parmar, Statistics in Medicine, 1994). Like
many Bayesian techniques, they were originally presented as
reanalyses of trials that have already been published. They
are now being used `live' (Fayers, Ashby and Parmar, Statistics
in Medicine, 1997). We present details of their implementation
in a trial of surgery with and without pre-operative chemotherapy
for oesophageal cancer.
Practical issues encountered will be discussed, including
the choice of prior distribution and `clinically relevant'
values for use in monitoring, and presentation of ideas to
clinical members of the data monitoring committee. As one
clinician put it `It's just casting a sceptical eye over the data'.

oooOOOooo


"Results on D-Optimal Designs for Weighted Regression
and Binary Regression Models".

Ben Torsney

We consider initially designing for weighted regression models
in one design variable x (with a 'constant' term). Weight
functions considered include established choices in the
literature - choices for which the weight function is
bounded when x is not. A 'widest' possible  design
interval, X, can be an unbounded one.  Ford, Torsney and
Wu (JRSSB 1992) revealed a further class of such weight
functions, in establishing that a design problem for a
binary regression model (and other generalised linear
models) can be transformed to designing for a weighted
regression model. These new weight functions are bounded
for all real x, so that we can allow X = R.  D-optimal
designs for all possible interval subsets of X are derived
under conditions on the weight function which are satisfied
by a large class. These designs are not simply linear shifts
of the optimal design on X, This conflicts with hints in the
literature.  The results extend to higher order models.
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				PLEASE NOTE
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