Article type
Year
Abstract
Background: Quality assessment of included studies is a crucial step in any systematic review (SR). Review and synthesis of prediction modelling studies is evolving and a tool facilitating quality assessment for prognostic and diagnostic prediction modelling studies is needed.
Objectives: To introduce PROBAST, a tool for assessing the risk of bias and applicability of prediction modelling studies in a SR.
Methods: A Delphi process, involving 40 prediction research experts was used until agreement on the content of the final tool was reached. Existing initiatives such as the REMARK and TRIPOD reporting guidelines for prediction research formed part of the evidence base for the tool development. The scope of PROBAST was determined with consideration of existing tools such as QUIPS and QUADAS 2.
Results: After six rounds of the Delphi procedure, a final tool was developed which utilises a domain-based structure supported by signalling questions similar to QUADAS 2. PROBAST assesses the risk of bias and applicability of prediction modelling studies. Risk of bias refers to any shortcomings in the study design, conduct or analysis leading to systematically distorted estimates of predictive performance or an inadequate model to address the research question. The predictive performance is typically evaluated using calibration, discrimination and sometimes classification measures. Assessment of applicability examines whether the prediction model development or validation study matches the systematic review question in terms of the target population, predictors, or outcomes of interest
PROBAST comprises four domains (Participant selection; Predictors; Outcome; Analysis) and 20 signalling questions grouped within these domains.
Conclusions: PROBAST can be used to assess the quality of prediction modelling studies included in a SR. The presentation will give an overview of the development process and introduce the final tool.
Patient or healthcare consumer involvement: A wide range of stakeholders was involved in the development and testing of PROBAST resulting in a tool that is methodologically sound, user-friendly, and relevant in various contexts. Stakeholders are directly involved in producing guidelines and shared-decision making tools, i.e. we envisage that PROBAST will be used in relevant research projects supporting and involving healthcare consumers.
Objectives: To introduce PROBAST, a tool for assessing the risk of bias and applicability of prediction modelling studies in a SR.
Methods: A Delphi process, involving 40 prediction research experts was used until agreement on the content of the final tool was reached. Existing initiatives such as the REMARK and TRIPOD reporting guidelines for prediction research formed part of the evidence base for the tool development. The scope of PROBAST was determined with consideration of existing tools such as QUIPS and QUADAS 2.
Results: After six rounds of the Delphi procedure, a final tool was developed which utilises a domain-based structure supported by signalling questions similar to QUADAS 2. PROBAST assesses the risk of bias and applicability of prediction modelling studies. Risk of bias refers to any shortcomings in the study design, conduct or analysis leading to systematically distorted estimates of predictive performance or an inadequate model to address the research question. The predictive performance is typically evaluated using calibration, discrimination and sometimes classification measures. Assessment of applicability examines whether the prediction model development or validation study matches the systematic review question in terms of the target population, predictors, or outcomes of interest
PROBAST comprises four domains (Participant selection; Predictors; Outcome; Analysis) and 20 signalling questions grouped within these domains.
Conclusions: PROBAST can be used to assess the quality of prediction modelling studies included in a SR. The presentation will give an overview of the development process and introduce the final tool.
Patient or healthcare consumer involvement: A wide range of stakeholders was involved in the development and testing of PROBAST resulting in a tool that is methodologically sound, user-friendly, and relevant in various contexts. Stakeholders are directly involved in producing guidelines and shared-decision making tools, i.e. we envisage that PROBAST will be used in relevant research projects supporting and involving healthcare consumers.