A case for the use of receiver operating characteristic analysis of potential clinical efficacy biomarkers in advanced renal cell carcinoma.
English Patricia A,Williams J Andrew,Martini Jean-François,Motzer Robert J,Valota Olga,Buller Richard E
Future oncology (London, England)
AIM:Assess patient-level utility of suggested pretreatment biomarkers of sunitinib in advanced renal cell carcinoma. PATIENTS & METHODS:Kaplan-Meier analysis of data from a randomized, Phase II study (n = 292) suggested baseline predictive value for circulating soluble Ang-2 and MMP-2 and HIF-1α percentage of tumor expression. Using this dataset, the sensitivity, specificity and area under the curve (AUC) were calculated, using receiver operating characteristic (ROC) curves. RESULTS:Based on a ROC (sensitivity vs 1 - specificity) threshold AUC value of >0.8, neither Ang-2 (0.67) nor MMP-2 (0.65), nor HIF-1α percentage of tumor expression (0.65), performed appropriately from a patient-selection standpoint. CONCLUSION:To properly assess potential biomarkers, sensitivity and specificity characteristics should be obtained by ROC analysis.
Combining multiple biomarkers linearly to maximize the partial area under the ROC curve.
Yan Qingxiang,Bantis Leonidas E,Stanford Janet L,Feng Ziding
Statistics in medicine
It is now common in clinical practice to make clinical decisions based on combinations of multiple biomarkers. In this paper, we propose new approaches for combining multiple biomarkers linearly to maximize the partial area under the receiver operating characteristic curve (pAUC). The parametric and nonparametric methods that have been developed for this purpose have limitations. When the biomarker values for populations with and without a given disease follow a multivariate normal distribution, it is easy to implement our proposed parametric approach, which adopts an alternative analytic expression of the pAUC. When normality assumptions are violated, a kernel-based approach is presented, which handles multiple biomarkers simultaneously. We evaluated the proposed as well as existing methods through simulations and discovered that when the covariance matrices for the disease and nondisease samples are disproportional, traditional methods (such as the logistic regression) are more likely to fail to maximize the pAUC while the proposed methods are more robust. The proposed approaches are illustrated through application to a prostate cancer data set, and a rank-based leave-one-out cross-validation procedure is proposed to obtain a realistic estimate of the pAUC when there is no independent validation set available.
Time-dependent ROC curve analysis in medical research: current methods and applications.
Kamarudin Adina Najwa,Cox Trevor,Kolamunnage-Dona Ruwanthi
BMC medical research methodology
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.
The ROC curve for regularly measured longitudinal biomarkers.
Michael Haben,Tian Lu,Ghebremichael Musie
Biostatistics (Oxford, England)
The receiver operating characteristic (ROC) curve is a commonly used graphical summary of the discriminative capacity of a thresholded continuous scoring system for a binary outcome. Estimation and inference procedures for the ROC curve are well-studied in the cross-sectional setting. However, there is a paucity of research when both biomarker measurements and disease status are observed longitudinally. In a motivating example, we are interested in characterizing the value of longitudinally measured CD4 counts for predicting the presence or absence of a transient spike in HIV viral load, also time-dependent. The existing method neither appropriately characterizes the diagnostic value of observed CD4 counts nor efficiently uses status history in predicting the current spike status. We propose to jointly model the binary status as a Markov chain and the biomarkers levels, conditional on the binary status, as an autoregressive process, yielding a dynamic scoring procedure for predicting the occurrence of a spike. Based on the resulting prediction rule, we propose several natural extensions of the ROC curve to the longitudinal setting and describe procedures for statistical inference. Lastly, extensive simulations have been conducted to examine the small sample operational characteristics of the proposed methods.