In:
BMC Medical Research Methodology, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2019-12)
Abstract:
The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements. Methods The proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis. Results From the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000–2005) to 16.0/year (2016–2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from − 0.048 to 0.217 in favour of models that utilize repeated measurements. Conclusions Repeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.
Type of Medium:
Online Resource
ISSN:
1471-2288
DOI:
10.1186/s12874-019-0847-0
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2019
detail.hit.zdb_id:
2041362-2
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