New PDP Webinar on November 5th: What is evidence and what does it mean for individual patients?
By convention, clinical trials, systematic reviews and observational studies provide clinical evidence for treatment recommendations.
When interpreting this evidence in the context of an individual patient there are two main questions; can the population-based evidence be applied and how favourable is the benefit to harm ratio of the recommendation?
The discussion will use some recent examples from the literature and address how a balanced viewpoint can be expressed when writing summaries or reports.
About the presenter
David Woods is a consultant pharmacist with a background in medicines information, pharmacy education and paediatric clinical pharmacy. David is currently pharmaceutical adviser to the Best Practice Advocacy Centre (bpacnz) and is a Professional Practice Fellow in the School of Pharmacy at the University of Otago. He has worked extensively in the field of evidence-based medicine and rational drug use and has contributed to educational and resource development programs both nationally and internationally, particularly in developing countries. Consultancies include work with the WHO, USAID, UNICEF and GRIP (Global Research in Paediatrics). David was integral to the development of the New Zealand Formulary and New Zealand Formulary for Children and recently directed the introduction of the national drug formulary for Kazakhstan.
Register online now
- Date: Monday, 5 November, 2018
- Time: 12 pm (Sydney time), 2 pm (New Zealand time)
- Register: Register online now
Upcoming Webinar - Statistics for Medical Writers
AMWA is pleased to bring you the first webinar of 2026. Presented by Dr Richard Kay, this webinar will give you a clear, logical structure
for thinking about statistical methodology in pharmaceutical R&D. You'll be introduced to methods used in different settings, which are
driven by the endpoint type, with a particular focus on binary (odds ratios, relative risks) and time to event (Kaplan-Meier curves, hazard
ratios) endpoints. Ideas behind p-values, confidence intervals, and powering a study to detect important levels of effect will be covered.
Each concept will be illustrated with case studies.
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