17/05/2018 – Better predictions using big(ger) data sets
Thursday 17/05/2018 at 15:00 in room B1.09
Thomas Debray from the UMCU will host the next MSDSlab. He will discuss how we can investigate, quantify and improve the generalizability of prediction models by utilizing big datasets from e-health records or meta-analyses with individual participant data.
Preparation: Have a look at the background readings
Clinical prediction models (CPM) 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 CPM predict 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.
Background literature (assessing generalizability of clinical prediction models)
All are optional. For novices I would recommend the BMJ and PLOS MED paper.
- Riley RD, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016;353:i3140. (Riley2016a)
- Debray TPA, et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol. 2015;68(3):279–89. (debray_new_2015)
- Debray TPA, et al. Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use. PLoS Med. 2015;12(10):e1001886. (Debray2015c)
- Debray TPA, et al. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013 Aug 15;32(18):3158–80. (Debray2012b)
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