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Putting risk prediction in perspective: relative utility curves.

ABSTRACT: Risk prediction models based on medical history or results of tests are increasingly common in the cancer literature. An important use of these models is to make treatment decisions on the basis of estimated risk. The relative utility curve is a simple method for evaluating risk prediction in a medical decision-making framework. Relative utility curves have three attractive features for the evaluation of risk prediction models. First, they put risk prediction into perspective because relative utility is the fraction of the expected utility of perfect prediction obtained by the risk prediction model at the optimal cut point. Second, they do not require precise specification of harms and benefits because relative utility is plotted against a summary measure of harms and benefits (ie, the risk threshold). Third, they are easy to compute from standard tables of data found in many articles on risk prediction. An important use of relative utility curves is to evaluate the addition of a risk factor to the risk prediction model. To illustrate an application of relative utility curves, an analysis was performed on previously published data involving the addition of breast density to a risk prediction model for invasive breast cancer.

SUBMITTER: Baker SG

PROVIDER: S-EPMC2778669 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Putting risk prediction in perspective: relative utility curves.

Baker Stuart G SG

Journal of the National Cancer Institute 20091020 22

Risk prediction models based on medical history or results of tests are increasingly common in the cancer literature. An important use of these models is to make treatment decisions on the basis of estimated risk. The relative utility curve is a simple method for evaluating risk prediction in a medical decision-making framework. Relative utility curves have three attractive features for the evaluation of risk prediction models. First, they put risk prediction into perspective because relative ut ...[more]

PMID: 19843888

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Project description:BackgroundBreast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.MethodsWe included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8?years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10?years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.ResultsIn the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5?years, 0.52-0.74; at 10?years, 0.53-0.72). Calibration-in-the-large was?-0.13 (95% PI:?-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10?years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.ConclusionsWe developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Project description:BackgroundMicroRNAs (miRNAs) are small regulatory RNA that mediate RNA interference by binding to various mRNA target regions. There have been several computational methods for the identification of target mRNAs for miRNAs. However, these have considered all contributory features as scalar representations, primarily, as thermodynamic or sequence-based features. Further, a majority of these methods solely target canonical sites, which are sites with "seed" complementarity. Here, we present a machine-learning classification scheme, titled Avishkar, which captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves, separately for various input features, such as thermodynamic and sequence features. Further, we use a principled approach to uniformly model canonical and non-canonical seed matches, using a novel seed enrichment metric.ResultsWe demonstrate that large number of seed-match patterns have high enrichment values, conserved across species, and that majority of miRNA binding sites involve non-canonical matches, corroborating recent findings. Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data. Our model significantly outperforms all established methods, for both canonical and non-canonical sites. We achieve this while using a much larger candidate miRNA-mRNA interaction set than prior work.ConclusionsWe have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves, specifically about 20% better than the state-of-the-art, for different species (human or mouse), or different target types (canonical or non-canonical). To the best of our knowledge we provide the first distributed framework for microRNA target prediction based on Apache Hadoop and Spark.AvailabilityAll source code and data is publicly available at https://bitbucket.org/cellsandmachines/avishkar.

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Putting risk prediction in perspective: relative utility curves.

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Putting risk prediction in perspective: relative utility curves.

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