TY - JOUR
T1 - Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models
T2 - Journal of Clinical Epidemiology
AU - Campbell, W.
AU - Ganna, A.
AU - Ingelsson, E.
AU - Janssens, Acjw
N1 - ISI Document Delivery No.: CZ5FJ Times Cited: 2 Cited Reference Count: 16 Campbell, William Ganna, Andrea Ingelsson, Erik Janssens, A. Cecile J. W. Ganna, Andrea/0000-0002-8147-240X European Community's Seventh Framework Program (FP7), ENGAGE Consortium [HEALTH-F4-2007-201413]; Swedish Research Council [2012-1397]; Swedish Heart-Lung Foundation [20120197]; National Cancer Institute at the National Institutes of Health [HHSN261201200425P]; European Starting grant of the European Research Council [310884] This work was supported by The European Community's Seventh Framework Program (FP7/2007-2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413, the Swedish Research Council (project grant no. 2012-1397), the Swedish Heart-Lung Foundation (project grant no. 20120197), the National Cancer Institute at the National Institutes of Health (grant number HHSN261201200425P), and the European Starting grant of the European Research Council (#310884). 2 1 3 ELSEVIER SCIENCE INC NEW YORK J CLIN EPIDEMIOL
PY - 2016
Y1 - 2016
N2 - Objective: We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Study Design and Setting: Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. Results: We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. Conclusion: We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. (C) 2016 Elsevier Inc. All rights reserved.
AB - Objective: We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Study Design and Setting: Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. Results: We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. Conclusion: We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. (C) 2016 Elsevier Inc. All rights reserved.
U2 - 10.1016/j.jclinepi.2015.06.011
DO - 10.1016/j.jclinepi.2015.06.011
M3 - Article
C2 - 26119889
VL - 69
SP - 89
EP - 95
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
SN - 0895-4356
ER -