%0 Journal Article %1 Plate:2019:BMC-Med-Res-Methodol:31655567 %A Plate, J D J %A van de Leur, R R %A Leenen, L P H %A Hietbrink, F %A Peelen, L M %A Eijkemans, M J C %D 2019 %J BMC Med Res Methodol %K CorrelatedData statistics %N 1 %P 199-199 %R 10.1186/s12874-019-0847-0 %T Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis %U https://www.ncbi.nlm.nih.gov/pubmed/31655567 %V 19 %X 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.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.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.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.

@article{Plate:2019:BMC-Med-Res-Methodol:31655567, 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.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.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.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.}, added-at = {2019-11-18T07:55:16.000+0100}, author = {Plate, J D J and van de Leur, R R and Leenen, L P H and Hietbrink, F and Peelen, L M and Eijkemans, M J C}, biburl = {https://www.bibsonomy.org/bibtex/2e4c143c717eedac427a750b839daba70/jkd}, description = {Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis. - PubMed - NCBI}, doi = {10.1186/s12874-019-0847-0}, interhash = {bd9cf41dcf609d2bb7a8d23945c89b61}, intrahash = {e4c143c717eedac427a750b839daba70}, journal = {BMC Med Res Methodol}, keywords = {CorrelatedData statistics}, month = oct, number = 1, pages = {199-199}, pmid = {31655567}, timestamp = {2019-11-18T07:55:16.000+0100}, title = {Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31655567}, volume = 19, year = 2019 }