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Assessing the incremental predictive performance of novel biomarkers over standard predictors.

ABSTRACT: It is unclear to what extent the incremental predictive performance of a novel biomarker is impacted by the method used to control for standard predictors. We investigated whether adding a biomarker to a model with a published risk score overestimates its incremental performance as compared to adding it to a multivariable model with individual predictors (or a composite risk score estimated from the sample of interest) and to a null model. We used 1000 simulated datasets (with a range of risk factor distributions and event rates) to compare these methods, using the continuous net reclassification index (NRI), the integrated discrimination index (IDI), and change in the C-statistic as discrimination metrics. The new biomarker was added to the following: null model, model including a published risk score, model including a composite risk score estimated from the sample of interest, and multivariable model with individual predictors. We observed a gradient in the incremental performance of the biomarker, with the null model resulting in the highest predictive performance of the biomarker and the model using individual predictors resulting in the lowest (mean increases in C-statistic between models without and with the biomarker: 0.261, 0.085, 0.030, and 0.031; NRI: 0.767, 0.621, 0.513, and 0.530; IDI: 0.153, 0.093, 0.053 and 0.057, respectively). These findings were supported by the Framingham Study data predicting atrial fibrillation using novel biomarkers. We recommend that authors report the effect of a new biomarker after controlling for standard predictors modeled as individual variables.

SUBMITTER: Xanthakis V

PROVIDER: S-EPMC4047140 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Assessing the incremental predictive performance of novel biomarkers over standard predictors.

Xanthakis Vanessa V Sullivan Lisa M LM Vasan Ramachandran S RS Benjamin Emelia J EJ Massaro Joseph M JM D'Agostino Ralph B RB Pencina Michael J MJ

Statistics in medicine 20140409 15

It is unclear to what extent the incremental predictive performance of a novel biomarker is impacted by the method used to control for standard predictors. We investigated whether adding a biomarker to a model with a published risk score overestimates its incremental performance as compared to adding it to a multivariable model with individual predictors (or a composite risk score estimated from the sample of interest) and to a null model. We used 1000 simulated datasets (with a range of risk fa ...[more]

PMID: 24719270

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Project description:PurposePhase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables.MethodsConsecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM).ResultsIn a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1).ConclusionStress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.

Project description:Over the last decades, various predictors have proven relevant for job performance [e.g., general mental ability (GMA), broad personality traits, such as the Big Five]. However, prediction of job performance is far from perfect, and further potentially relevant predictors need to be investigated. Narrower personality traits, such as individuals' character strengths, have emerged as meaningfully related to different aspects of job performance. However, it is still unclear whether character strengths can explain additional variance in job performance over and above already known powerful predictors. Consequently, the present study aimed at (1) examining the incremental validity of character strengths as predictors of job performance beyond GMA and/or the Big Five traits and (2) identifying the most important predictors of job performance out of the 24 character strengths, GMA, and the Big Five. Job performance was operationalized with multidimensional measures of both productive and counterproductive work behavior. A sample of 169 employees from different occupations completed web-based self-assessments on character strengths, GMA, and the Big Five. Additionally, the employees' supervisors provided web-based ratings of their job performance. Results showed that character strengths incrementally predicted job performance beyond GMA, the Big Five, or GMA plus the Big Five; explained variance increased up to 54.8, 43.1, and 38.4%, respectively, depending on the dimension of job performance. Exploratory relative weight analyses revealed that for each of the dimensions of job performance, at least one character strength explained a numerically higher amount of variance than GMA and the Big Five, except for individual task proactivity, where GMA exhibited the numerically highest amount of explained variance. The present study shows that character strengths are relevant predictors of job performance in addition to GMA and other conceptualizations of personality (i.e., the Big Five). This also highlights the role of socio-emotional skills, such as character strengths, for the understanding of performance outcomes above and beyond cognitive ability.

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Assessing the incremental predictive performance of novel biomarkers over standard predictors.

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Assessing the incremental predictive performance of novel biomarkers over standard predictors.

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