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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 – Residential course
Ponte di Legno, Brescia, Italy
FACULTY: Professor Patrick J. Heagerty
Department of Biostatistics
University of Washington
COORDINATORS:
Prof. Maria Grazia Valsecchi
Dott. Stefania Galimberti
Center of Biostatistics for Clinical Epidemiology
Department of Clinical Medicine and Prevention
University of Milano-Bicocca
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.
*** DEADLINE FOR REGISTRATION IS 21 JANUARY 2011 ***
For full details on the course, go to:
http://www.statmed.medicina.unimib.it/statisticalps2011/statisticalps.htm
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