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Mini Mavo 2020
Mini MAVO2020 is a unique online voiceover event that offers sessions on a va...
Mini MAVO2020 is a unique online voiceover event that offers sessions on a variety of industry topics, including commercial voiceover, animation, marketing, demos, home studio, the business side of VO, motivation, goal setting, volunteer opportunities, auditioning, audiobooks, telephony. The connections and networking opportunities offered during this event were outstanding. Enjoy the replay and catch us live next time we host another event!!
Saturday 29th May 00:00
This three-day statistical course provides a detailed foundation of the metho...
This three-day statistical course provides a detailed foundation of the methods and principles for meta-analysis when IPD (Individual Participant Data) are available from multiple related studies. The course considers continuous, binary and time-to-event outcomes, and covers a variety of modelling options, including fixed effect and random effects. Days 1 and 2 mainly focus on the synthesis of IPD from randomised trials of interventions, where the aim is to summarise a treatment effect or to examine treatment-covariate interactions. We outline how to use either a two-stage framework (day 1) or a one-stage framework (day 2) for the meta-analysis, and compare their pros and cons. Day 3 focuses on novel extensions including multivariate and network meta-analysis of IPD to incorporate correlated and indirect evidence (e.g. from multiple outcomes or multiple treatment comparisons). Special topics will also be covered, including: (i) IPD meta-analysis to identify prognostic/risk factors, (ii) IPD meta-analysis of test accuracy studies; (iii) estimating the power of a planned IPD meta-analysis; and (iv) dealing with unavailable IPD. The course consists of a mixture of lectures and practical sessions to reinforce the underlying statistical concepts. Participants can choose either Stata or R for the practicals. The key messages are illustrated with real examples throughout the course.
Monday 03rd May 10:00
Friday 07th May 18:00
Methods for Risk Predictions
Statistical methods for risk prediction and prognostic models...
Statistical methods for risk prediction and prognostic models
Day 1 covers key topics for model development 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.
Day 2 focuses on how models are optimised for the data in which they were derived, and thus often do not generalise to other datasets. Internal validation strategies are outlined to identify and adjust for overfitting. In particular, bootstrapping is covered to estimate the optimism and shrink the model coefficients accordingly; related approaches such as Lasso are also discussed. Statistical measures of model performance are introduced for discrimination (such as the C-statistic and D-statistic) and calibration (calibration-in-the-large, calibration plots, calibration slope and curves). Further sessions cover sample size considerations for model development and validation, and modern methods for shrinkage and penalisation.
Day 3 focuses on the need for model performance to be evaluated in new data to assess its generalisability, namely external validation. A framework for different types of external validation studies is provided, and the potential importance of model updating strategies (such as re-calibration techniques) are considered. Novel topics are then considered, including: the development and validation of models using large datasets (e.g. from e-health records) or multiple studies; the use of meta-analysis methods for summarising the performance of models across multiple studies or clusters; and the use of net benefit and decision curve analysis to understand the potential role of a model for clinical decision making. Practical guidance is then given about different ways in which prediction and prognostic models can be presented, and the final session discusses the importance of the TRIPOD reporting guideline when publishing prediction model research.
Stata and R practical exercises are included on all three days, and participants will be able to choose whether to focus on logistic regression examples (for binary outcomes) or Cox / flexible parametric survival examples (for time-to-event outcomes), to tailor these exercises to their own purpose.
Wednesday 19th May 09:00
Friday 21st May 18:00
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