Assessment of Claims of Improved Prediction Beyond the Framingham Risk Score

Tzoulaki, Ioanna; Liberopoulos, George; Ioannidis, John P. A.

doi:10.1001/jama.2009.1757

Cited by 248 publications

(212 citation statements)

References 102 publications

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“…Traditionally ROC curves have been used to compare additional markers (Tzoulaki, et al, 2009). A comparison of ROC curves for Models 1 and 2 has two downsides from a medical decision-making perspective.…”

Section: Graphical Insightsmentioning

confidence: 99%

“…A second approach is to construct a summary statistical measure of classification performance in the validation sample. Examples include a change in the area under the receiver operating characteristic curve (AUC) (Tzoulaki, et al, 2009), a difference in the maximum Youden indices, integrated discriminant improvement, and net reclassification improvement (Pencina, et al 2008, Gu and Pepe, 2009, Whittemore, 2010. A fundamental limitation with these approaches is deciding how large a difference between performance measures for Models 1 and Model 2 is sufficient to deem the new marker worthwhile.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update

Baker

Calster²

2012

The International Journal of Biostatistics

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Most of the methodological literature on evaluating an additional marker for risk prediction involves purely statistical measures of classification performance. A disadvantage of a purely statistical measure is the difficulty in deciding the improvement in the measure that would make inclusion of the additional marker worthwhile. In contrast, a medical decision making approach can weigh the cost or harm of ascertaining an additional marker against the benefit of a higher true positive rate for a given false positive rate that may be associated with risk prediction involving the additional marker. An appealing form of the medical decision making approach involves the risk threshold, which is the risk at which the expected utility of treatment and no treatment is the same. In this framework, a readily interpretable evaluation of the net benefit of an additional marker is the test tradeoff corresponding to the risk threshold. The test tradeoff is the minimum number of tests for a new marker that need to be traded for a true positive to yield an increase in the net benefit of risk prediction with the additional marker. For a sensitivity analysis the test tradeoff is computed over multiple risk thresholds. This article updates the theory and estimation of the test tradeoff. An example is provided.

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Section: Graphical Insightsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update

Baker

Calster²

2012

The International Journal of Biostatistics

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“…There is ample evidence that prediction research often suffers from poor design and bias, and these may also have an impact on the results of the studies and on the models of disease outcomes based on these studies [4][5][6]. Although most prognostic studies published to date claim significant results [7,8], very few translate to clinically useful applications. Just as for observational epidemiological studies [9], poor reporting complicates the use of the specific study for research, clinical or public health purposes and hampers the synthesis of evidence across studies.…”

Section: Introductionmentioning

confidence: 99%

Strengthening the reporting of genetic risk prediction studies: the GRIPS statement

Janssens

Ioannidis

Duijn

et al. 2011

European Journal of Clinical Investigation

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“…64 The median AUC for the widely investigated Framingham Risk Score, when coronary heart disease was the outcome examined in 57 studies, was 77% (interquartile range: 71-83%). 65 In the single study of the five-SNP panel that investigated mortality, there was no difference between SNP-based and non-SNP-based models. In the single study of the panel that addressed differences by Gleason score, as well as aggressive and nonaggressive disease, there was no association with scores derived from the five-SNP panel.…”

Section: Discussionmentioning

confidence: 98%

Multigene panels in prostate cancer risk assessment: a systematic review

Little

Wilson

Carter

et al. 2016

Genetics in Medicine

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Prostate cancer is the fourth most common malignancy worldwide, and the second most common among men.1 In 2014, it was estimated that more than a quarter of a million new cases were diagnosed in North America and that the disease accounted for more than 33,000 deaths. 2,3 These numbers are likely to increase with the aging of the population. On the basis of data from the Surveillance, Epidemiology, and End Results program, more men were diagnosed with prostate cancer at a younger age and earlier stage in 2004-2005 than in the mid-late 1990s, and disparity between ethnic groups in cancer stage at diagnosis decreased. 4 Apart from age, 5 ethnic group, 5,6 and family history, 7-9 the risk factors associated with prostate cancer are unclear, 5 making primary prevention difficult. Prostate cancer is currently considered to be a complex, multifactorial disease, and the vast majority of familial clustering is attributed to the interaction of multiple shared susceptibility genes with moderate to low penetrance and shared environmental factors within these families.The natural history of prostate cancer is highly variable. 10 In a large proportion of men the disease is indolent, and it is difficult to predict which tumors will be aggressive. The value of aggressive management for localized prostate cancer is debated, [11][12][13][14] and only a small proportion of men with early-stage prostate cancer die from the disease within 10 to 15 years of diagnosis. Developing tools to differentiate aggressive and indolent disease is of key importance.Prostate-specific antigen (PSA) screening was introduced in the late 1980s.15 Meta-analyses of seven randomized controlled trials (RCTs) of screening using PSA testing alone or in combination with digital rectal examination suggested no evidence of benefit in reducing mortality 16,17 and some evidence of harm from overdiagnosis.17 Amid substantial debate, 18-20 the argument has been made for developing more accurate screening tests, including possible genetic markers. Purpose: Single-nucleotide polymorphism (SNP) panel tests have been proposed for use in the detection of, and prediction of risk for, prostate cancer and as prognostic indicator in affected men. A systematic review was undertaken to address three research questions to evaluate the analytic validity, clinical validity, clinical utility, and prognostic validity of SNP-based panels.Methods: Data sources comprised MEDLINE, Cochrane CEN-TRAL, Cochrane Database of Systematic Reviews, and EMBASE; these were searched from inception to April 2013. The gray-literature searches included contact with manufacturers. Eligible studies included English-language studies evaluating commercially available SNP panels. Study selection and risk of bias assessment were undertaken by two independent reviewers. Results:Twenty-one studies met eligibility criteria. All focused on clinical validity and evaluated 18 individual panels with 2 to 35 SNPs.All had poor discriminative ability (overall area under receiver-operator characteristic curves, 5...

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Assessment of Claims of Improved Prediction Beyond the Framingham Risk Score

Cited by 248 publications

References 102 publications

Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update

Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update

Strengthening the reporting of genetic risk prediction studies: the GRIPS statement

Multigene panels in prostate cancer risk assessment: a systematic review

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