Article type
Year
Abstract
Background: Quality assessment of included studies is a crucial step in any systematic review. Review and synthesis of prediction modelling studies is a relatively new and evolving area. The QUIPS tool for prediction finding studies has been recently updated. However, a tool facilitating quality assessment for prognostic and diagnostic prediction modelling studies is needed.
Objectives: To develop PROBAST, a tool for assessing the risk of bias and applicability of prediction modelling studies.
Methods: Risk of bias addresses the extent to which reported estimates of the predictive performance/accuracy (e.g. discrimination, calibration and (re)classification estimates) of the prediction model are potentially biased. Applicability refers to the extent to which the reported prediction model and the population used to measure model performance matches the review question and intended use of the model. For PROBAST, we have adopted a domain-based structure supported by signalling questions similar to QUADAS-2, which assesses risk of bias in diagnostic studies. We are using a Delphi process to develop PROBAST. Existing initiatives in the field of prediction research such as the REMARK (Reporting Recommendations for Tumor Marker Prognostic Studies) guidelines and the TRIPOD prediction model reporting guidelines formed part of the evidence base for the tool development. The scope of PROBAST was determined with consideration of existing tools, such as QUIPS. Forty experts and review authors in the field of prediction research are taking part in the Delphi process. We anticipate about five rounds of this process will be needed until agreement on the content of the final tool.
Results and Conclusions: The first rounds developing domains are now completed. The presentation will give an overview of the process, the current version of the tool (including the addressed domains and signalling questions) as well as an insight into underlying discussions.
Objectives: To develop PROBAST, a tool for assessing the risk of bias and applicability of prediction modelling studies.
Methods: Risk of bias addresses the extent to which reported estimates of the predictive performance/accuracy (e.g. discrimination, calibration and (re)classification estimates) of the prediction model are potentially biased. Applicability refers to the extent to which the reported prediction model and the population used to measure model performance matches the review question and intended use of the model. For PROBAST, we have adopted a domain-based structure supported by signalling questions similar to QUADAS-2, which assesses risk of bias in diagnostic studies. We are using a Delphi process to develop PROBAST. Existing initiatives in the field of prediction research such as the REMARK (Reporting Recommendations for Tumor Marker Prognostic Studies) guidelines and the TRIPOD prediction model reporting guidelines formed part of the evidence base for the tool development. The scope of PROBAST was determined with consideration of existing tools, such as QUIPS. Forty experts and review authors in the field of prediction research are taking part in the Delphi process. We anticipate about five rounds of this process will be needed until agreement on the content of the final tool.
Results and Conclusions: The first rounds developing domains are now completed. The presentation will give an overview of the process, the current version of the tool (including the addressed domains and signalling questions) as well as an insight into underlying discussions.