Below is a limited overview of future events (e.g. courses, invited talks, workshops, seminars).
Clinical Prediction Models and the role of Evidence Synthesis
- 5:00 PM TO 6:20 PM
- DAGStat Conference 2019
- Munich, Germany
Clinical prediction models are an important tool in contemporary medical decision making and abundant in the medical literature. These models estimate the probability/risk that a certain condition is present or will occur in the future by combining information from multiple variables (predictors) from an individual, e.g. predictors from patient history, physical examination or medical testing. Unfortunately, many prediction models perform much worse than anticipated during their development. A major reason for unsatisfactory performance and limited use in clinical practice is that they are typically developed from relatively small datasets, and subsequently used in populations/settings too different from the original development population/setting, without proper validation and adaptation to the new situation. In this talk, I will discuss how we can investigate, quantify and improve the generalizability of prediction models by adopting formal strategies for evidence synthesis. I will highlight the potential advantages of undertaking a systematic review, and present statistical methods to build upon published evidence or multiple sources of individual participant data when developing or validating a prediction model.
- Épidémiologie clinique et biostatistique: le congrès des chercheurs et professionnels francophones
- Toulouse, France
No abstract available yet. More information available on the EPICLIN website.
Systematic reviews & meta-analysis of prognosis studies
- NHMRC Clinical Trials Centre
- Sydney, Australia
This two day course will cover the principles, design, searching, data extraction and risk of bias assessment in review of prognosis studies, and is open to everyone with interest in systematic reviews of prognosis studies. An additional third day will address the more advanced topic of meta-analysis of prognosis studies, including computer exercises where two meta-analyses are conducted.
Contact Matthew Wynn (email@example.com) to be informed of updates and registration details.
Multiple imputation of multilevel data
- 9:00 AM TO 12:30 PM
- 40th Annual Conference of the International Society for Clinical Biostatistics
- Leuven, Belgium
Multiple imputation is widely used to handle missing data, but standard implementations assume independent data. Recent developments enable imputation of multilevel (clustered) data, such as data from multi-centre studies and individual participant data meta-analysis. This course describes the difficulties in handling missing values in such data: notably the challenge of systematically missing data (where a variable is missing for all individuals in a cluster), and the importance of respecting the hierarchical structure of the data. We will give some theoretical background and show how the imputation model must be tailored to the intended form of analysis. We will then describe the two main families of imputation methods for multilevel data that are available in statistical software packages, joint modelling and chained equations (fully conditional specification), and summarise their strengths and weaknesses. The course will end with a practical session in which participants may apply the methods in R to data that we provide, and/or have further discussion.
By the end of the course, participants should understand the difficulties of multiply imputing multilevel data, understand the strengths and weaknesses of two main families of imputation methods, and be able to apply them to their own data.