ABS08 - 2008 Applied Bayesian Statistics School

BAYESIAN DECISION PROBLEMS IN BIOSTATISTICS AND CLINICAL TRIALS

Centro Congressi Panorama, Trento, Italy

READING


There is no required course text but we recommend the following books for reference and/or background reading before the course. Book (1) is background and review, books (2) and (3) cover the main concepts presented in the course, and material from book (4) discusses the models covered on Thursday (9:00-10:30 and 16:00-17:30 sessions).

  • (1) Statistical inference

    H.S. Migon and D. Gamerman, Statistical Inference: An Integrated Approach , Hodder Arnold, 1999

    In particular, Chapters 4 (estimation), 5.5 (MCMC), 7 (Prediction), 8 (linear models) would make a good and quick review. The course assumes basic familiarity with statistical inference at this level.

  • (2) Biostatistics

    Spiegelhalter, Abrams and Myles, Bayesian Approaches to Clinical Trials and Health-care Evaluation, Wiley, 2004.

    We will cover (partially) the following chapters
  • 3.1-10 (Bayes, Monday morning);
  • 3.13 (Preditive inference, Tuesday morning)
  • 3.16-17 (Hierarchical models, Tuesday morning)
  • 6.1-7 (Clinical trials, Monday morning)

  • (3) Computing

    Albert, Bayesian Computation with R, Springer, 2007.

    In its first two chapters it is possible to find an introduction to the R software environment for statistical computing and graphics, and to Bayesian reasoning as well. Furthermore, a rich documentation about R is available on the R Project Website .

  • (4) Analysis of microarray data

    Parmigiani, Garrett, Irizarry and Zeger, The analysis of gene expression data, Springer, 2003.

    It will be used for Chapters 1, 11 and 16 (Thursday). More information on the book can be found here.

  • Material beyond these references includes specific Bayesian clinical trial designs (Monday and Tuesday afternoon), sequential design (Wednesday), multiplicities (Thursday morning) and ROC curves (Friday morning).