Statistical Methods for Risk Prediction & Prognostic Models
Academic (public sector)
Internal - Primary Care Sciences Staff & Students
For accommodation and logistical queries please contact Sue Weir, e-mail, firstname.lastname@example.org or telephone 01782 733922
For further information on course content contact Dr Kym Snell, e-mail email@example.com
If you wish to book any extra accommodation please get in touch with Keele Management Centre on: 01782 738900 or 01782 732020
TARGET AUDIENCE & REQUIREMENTS: The course is aimed at individuals that want to learn how to develop and validate risk prediction and prognostic models, specifically for binary or time-to-event clinical outcomes. We recommend participants have a background in statistics. An understanding of key statistical principles and measures (such as effect estimates, confidence intervals and p-values) and the ability to apply and interpret regression models is essential. We also recommend that participants are familiar with Stata, although the practicals will not require individuals to write their own code. Participants will need to bring a laptop with Stata version 12 or above installed. It may be possible to borrow a laptop on the day, but this must be agreed in advance.
OVERVIEW OF COURSE CONTENT: The course is delivered over 3 days, and focuses on model development (day 1), internal validation (day 2), and external validation and novel topics (day 3). Our focus is on multivariable models for individualised prediction of future outcomes (prognosis), although many of the concepts described also apply to models for predicting existing disease (diagnosis).
Day 1 begins with an overview of the rationale and phases of prediction model research. It then outlines model specification, focusing on logistic regression for binary outcomes and Cox regression or flexible parametric survival models for time to event outcomes. Model development topics are then covered, including: identifying candidate predictors, handling of missing data, modelling continuous predictors using fractional polynomials or restricted cubic splines for non-linear functions, and variable selection procedures.
CANCELLATION & REPLACEMENT POLICY
Any registration amendments and/or cancellations must be notified in writing to the Course Administrator Sue Weir (firstname.lastname@example.org).
Course registration cancellations received in writing before 2nd November 2017 will be refunded in full less an administrative fee of 20% of the registration amount. Cancellations received after that date will not receive refunds. Substitute delegates are welcome without penalty up until two weeks before the Course begins, but please advise the Conference Administrator of the changes.
In the event that the meeting is cancelled by the organisers, or if for any reason or any factor outside the control of the organisers the conference cannot take place, the amount of the registration fee shall be refunded, less an administrative fee of 20% of the registration amount. The liability of the organisers shall be limited to that refund and neither shall be liable for any other loss, cost or expense, however caused, incurred or arising. In particular, neither the organisers shall be liable to refund any travel or hotel costs incurred by delegates or their organisations.
No-shows or cancellations after 2nd November 2017 will not be reimbursed.