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List of contents
- Part 1: Introduction to prognosis and prognosis research
- 1: Peter Croft, Richard D. Riley, Danielle A. van der Windt, and Karel G.M. Moons: Prognosis in healthcare
- 2: Peter Croft, Richard D. Riley, Danielle A. van der Windt, Karel G.M. Moons, and Harry Hemingway: A framework for prognosis research
- Part 2: Fundamental statistics for prognosis research
- 3: Richard D. Riley, Kym I.E. Snell, Karel G.M. Moons, and Thomas P.A. Debray: Fundamental statistical methods for prognosis research
- 4: Richard D. Riley, Kym I.E. Snell, Karel G.M. Moons, and Thomas P.A. Debray: Ten principles to strengthen prognosis research
- Part 3: Undertaking prognosis research
- 5: Danielle A. van der Windt, Harry Hemingway, Peter Croft: Overall prognosis research
- 6: Richard D. Riley, Karel G.M. Moons, Jill A. Hayden, Willi Sauerbrei, Douglas G. Altman: Prognostic factor research
- 7: Richard D. Riley, Karel G.M. Moons, Thomas P.A. Debray, Kym I.E. Snell, Ewout W. Steyerberg, Douglas G. Altman, Gary S. Collins: Prognostic model research
- 8: Danielle A. van der Windt, Richard D. Riley, Aroon Hingorani, Karel G.M. Moons: Predictors of treatment effect
- 9: Richard D. Riley, Karel G.M. Moons, Thomas P.A. Debray, Douglas G. Altman, Gary S. Collins: Systematic reviews and meta-analysis of prognosis research studies
- Part 4: Exemplars of prognosis research impact
- 10: Nadine E Foster, Danielle A. van der Windt, Kate M. Dunn, Peter Croft: Prognosis research in people with low back pain
- 11: Adam Timmis, Pablo Perel, Peter Croft: Prognosis research in people with coronary heart disease
- 12: Katherine I. Morley, Pablo Perel: Prognosis research in people with traumatic bleeding
- Part 5: Novel Topics in prognosis research
- 13: Richard D. Riley, Thomas P.A. Debray, Karel G.M.Moons: Individual participant data meta-analysis of prognosis studies
- 14: Kelvin P. Jordan, Karel G.M. Moons: Electronic healthcare records and prognosis research
- 15: Michael J. Crowther, Mark J Rutherford: Novel statistical methods for prognosis research
- 16: Mihaela van der Schaar, Harry Hemingway: Machine learning in prognosis research
About the author
Richard D. Riley is a Professor of Biostatistics at Keele University since 2014, having previous held posts at the Universities of Birmingham, Liverpool and Leicester. He is focused on statistical and methodological research for prognosis and meta-analysis, and supports clinical projects in these areas. He is also a Statistics Editor for the BMJ and a co-convenor of the Cochrane Prognosis Methods Group. Prof Riley co-leads a summer school in Prognosis Research Methods, and leads a number of statistical training courses for risk prediction and meta-analysis
Professor Danielle van der Windt received her academic training in epidemiology in the Netherlands at the EMGO+ Institute for Health and Care Research of the VU University in Amsterdam where she worked in a programme of research on the diagnosis, prognosis and management of musculoskeletal disorders. She is currently a Professor of primary care epidemiology at Keele, and is part of the Centre's executive management team
Professor Peter Croft is a Professor of Primary Care Epidemiology at Keele since 1994. Previously, he worked as a General Practitioner in Newcastle-under-Lyme, before training as an epidemiologist at the Medical Research Council's Environmental Epidemiology Unit in Southampton, and at the Arthritis Research Campaign's Epidemiology Research Unit in Manchester
Professor Karel G.M. Moons is Professor of Clinical Epidemiology at the Julius Center for Health Sciences and Primary Care. He is Director of Research in the management team of the Julius Center, and leads the research programme 'Methodology'. Since 2005 also he has an Adjunct Professorship at VanderBilt University, Nashville, USA. Having obtained his PhD in Epidemiology at Erasmus University, Rotterdam, he has been Visiting Professor at the University of Virginia, Charlottesville, USA in 2002, and at Tokai University, Japan.
Summary
This book is an introduction to the field of prognosis, and a discussion of how the information collected during prognosis research can be used to predict an individual patient's outcome. It looks at how we can develop target treatments based on prognosis research.
