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Predictive medicine is a field of medicine that entails predicting the probability of disease and instituting preventive measures in order to either prevent the disease altogether or significantly decrease its impact upon the patient.
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In genetics, a polygenic score (PGS), also called a polygenic risk score (PRS), genetic risk score, or genome-wide score, is a number that summarises the estimated effect of many genetic variants on an individual's phenotype, typically calculated as a weighted sum of trait-associated alleles. It reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. In other words, it gives an estimate of how likely an individual is to have a given trait only based on genetics, without taking environmental factors into account. Polygenic scores are widely used in animal breeding and plant breeding due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from genome-wide association study (GWAS) data.
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In machine learning, a given algorithm is said to be fair, or to have fairness if its results are independent of given variables, especially those considered sensitive, such as the traits of individuals which should not correlate with the outcome.
Decision curve analysis evaluates a predictor for an event as a probability threshold is varied, typically by showing a graphical plot of net benefit against threshold probability. By convention, the default strategies of assuming that all or no observations are positive are also plotted. Threshold probability is defined as the minimum probability of an event at which a decision-maker would take a given action, for instance, the probability of cancer at which a doctor would order a biopsy. A lower threshold probability implies a greater concern about the event, while a higher threshold implies greater concern about the action to be taken. Net benefit is a weighted combination of true and false positives, where the weight is derived from the threshold probability. The predictor could be a binary classifier, or a percentage risk from a prediction model, in which case a positive classification is defined by whether predicted probability is at least as great as the threshold probability.
References
- ↑ Leening MJG, Vedder MM, Witteman JCM, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160(2):122-131.
- ↑ 1. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928-935.
- ↑ Pencina MJ, D’Agostino RB, Pencina KM, Janssens ACJW, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012;176(6):473-481.
- ↑ Steyerberg EW, Calster BV, Pencina MJ. Performance Measures for Prediction Models and Markers: Evaluation of Predictions and Classifications. Revista Española de Cardiología (English Edition). 2011;64(9):788-794
- ↑ Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157-172; discussion 207-212.
- ↑ Pencina, M.J., D'Agostino Sr, R.B. and Steyerberg, E.W., 2011. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in medicine, 30(1), pp.11-21.
- ↑ Pepe, M. S., Fan, J., Feng, Z., Gerds, T., & Hilden, J. (2015). The net reclassification index (NRI): a misleading measure of prediction improvement even with independent test data sets. Statistics in biosciences, 7(2), 282-295.
- ↑ Kundu S, Aulchenko YS, van Duijn CM, Janssens AC. PredictABEL: an R package for the assessment of risk prediction models. European journal of epidemiology. 2011 Apr;26(4):261-4.
- ↑ Uno, H. and Cai, T., 2013. survIDINRI: IDI and NRI for comparing competing risk prediction models with censored survival data. R. Package Version, p.1.