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Time-dependent ROC curve analysis in medical research: current methods and applications

View Article: PubMed Central - PubMed

ABSTRACT

Background: ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker.

Methods: We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver.

Results: From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking.

Conclusions: The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research.

Electronic supplementary material: The online version of this article (doi:10.1186/s12874-017-0332-6) contains supplementary material, which is available to authorized users.

No MeSH data available.


Time-dependent ROC curves for 0, 1, 3, 5 years prior to death for the marker measured at visit time at ten years
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Fig2: Time-dependent ROC curves for 0, 1, 3, 5 years prior to death for the marker measured at visit time at ten years

Mentions: The corresponding ROC curves are shown in Fig. 2 for 0, 1, 3 and 5 years prior to death at visit time at 10 years (year 0 implies that the death is occurred at 10 years since enrolment to the study). Figure 2 clearly shows that the discrimination is better when the marker is measured at times closer to death. The estimated AUC value for five years before death is about 0.5, hence it can be concluded that the marker is useless to be used for discrimination between cases and controls at five years before death.Fig. 2


Time-dependent ROC curve analysis in medical research: current methods and applications
Time-dependent ROC curves for 0, 1, 3, 5 years prior to death for the marker measured at visit time at ten years
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5384160&req=5

Fig2: Time-dependent ROC curves for 0, 1, 3, 5 years prior to death for the marker measured at visit time at ten years
Mentions: The corresponding ROC curves are shown in Fig. 2 for 0, 1, 3 and 5 years prior to death at visit time at 10 years (year 0 implies that the death is occurred at 10 years since enrolment to the study). Figure 2 clearly shows that the discrimination is better when the marker is measured at times closer to death. The estimated AUC value for five years before death is about 0.5, hence it can be concluded that the marker is useless to be used for discrimination between cases and controls at five years before death.Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker.

Methods: We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver.

Results: From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking.

Conclusions: The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research.

Electronic supplementary material: The online version of this article (doi:10.1186/s12874-017-0332-6) contains supplementary material, which is available to authorized users.

No MeSH data available.