[Forum SIS] Course on "Longitudinal data analysis with time-dependent covariates for inference and prediction" Prof. Patrick J. Heagerty, 13-17 March 2011, Ponte di Legno (BS)

[Stefania Galimberti]

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Third Edition of STATISTICALPS: Winter course on medical statistics.in the
Alps

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LONGITUDINAL DATA ANALYSIS WITH TIME-DEPENDENT COVARIATES FOR INFERENCE AND
PREDICTION

13-17 March 2011, Ponte di Legno (BS)

 

FACULTY: Professor Patrick J. Heagerty 

   Department of Biostatistics

   University of Washington, Seattle (USA)

 

COORDINATORS: 

Prof. Maria Grazia Valsecchi

Dott. Stefania Galimberti

Center of Biostatistics for Clinical Epidemiology

Department of Clinical Medicine and Prevention

University of Milano-Bicocca

 

*** DEADLINE FOR REGISTRATION IS 21 JANUARY 2011 ***

 

For full details on the course, go to:

http://www.statmed.medicina.unimib.it/statisticalps2011/statisticalps.htm

 

 

ABSTRACT

Longitudinal studies allow investigators to correlate changes in
time-dependent exposures or biomarkers with subsequent health outcomes.
However, there are two key statistical challenges associated with the use of
time-dependent predictive information. First, inference regarding the impact
of time-dependent covariates on subsequent repeated measures outcomes
requires consideration of the factors that drive the change in covariates.
Statistical methods appropriate for analysis of time-dependent covariates
have expanded to include causal inference methods derived from both biometry
and econometrics.  Second, the use of time-dependent markers to predict a
subsequent change in clinical status, such as transition to a diseased
state, requires the formulation of appropriate prediction error concepts.

The first part of the course will review longitudinal data analysis methods
recently developed for valid inference regarding time-dependent covariates.
The second part of the course will introduce predictive accuracy concepts
that allow evaluation of time-dependent sensitivity and specificity for
prognosis of a subsequent event time. We will overview options that are
appropriate for both baseline markers and longitudinal markers. Methods will
be illustrated using examples from medical research and R packages that are
currently available.

 

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