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  1. Article ; Online: ROC curves for clinical prediction models part 2. The ROC plot: the picture that could be worth a 1000 words.

    Janssens, A Cecile J W

    Journal of clinical epidemiology

    2020  Volume 126, Page(s) 217–219

    MeSH term(s) Area Under Curve ; Humans ; ROC Curve
    Language English
    Publishing date 2020-06-18
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 639306-8
    ISSN 1878-5921 ; 0895-4356
    ISSN (online) 1878-5921
    ISSN 0895-4356
    DOI 10.1016/j.jclinepi.2020.05.036
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  2. Article ; Online: ROC curves for clinical prediction models part 4. Selection of the risk threshold-once chosen, always the same?

    Janssens, A Cecile J W

    Journal of clinical epidemiology

    2020  Volume 126, Page(s) 224–225

    MeSH term(s) Area Under Curve ; Ethnic Groups ; Humans ; ROC Curve
    Language English
    Publishing date 2020-06-18
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 639306-8
    ISSN 1878-5921 ; 0895-4356
    ISSN (online) 1878-5921
    ISSN 0895-4356
    DOI 10.1016/j.jclinepi.2020.05.038
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  3. Article: Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

    Janssens, A Cecile J W

    Genes

    2019  Volume 10, Issue 6

    Abstract: Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make ...

    Abstract Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.
    MeSH term(s) Algorithms ; Commerce ; Consumer Behavior ; Decision Making, Organizational ; Disclosure ; Genetic Diseases, Inborn/epidemiology ; Genetic Diseases, Inborn/genetics ; Genetic Testing ; Humans ; Multifactorial Inheritance/genetics
    Language English
    Publishing date 2019-06-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527218-4
    ISSN 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes10060448
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  4. Article ; Online: Defining Evidence for Precision Medicine: A Patient Is More Than a Set of Covariates.

    Janssens, A Cecile J W

    Epidemiology (Cambridge, Mass.)

    2019  Volume 30, Issue 3, Page(s) 342–344

    MeSH term(s) Humans ; Precision Medicine
    Language English
    Publishing date 2019-02-21
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000000992
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  5. Article ; Online: Validity of polygenic risk scores: are we measuring what we think we are?

    Janssens, A Cecile J W

    Human molecular genetics

    2019  Volume 28, Issue R2, Page(s) R143–R150

    Abstract: Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as ... ...

    Abstract Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
    MeSH term(s) Alleles ; Diabetes Mellitus, Type 2/epidemiology ; Diabetes Mellitus, Type 2/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Genotype ; Humans ; Models, Genetic ; Multifactorial Inheritance ; Polymorphism, Single Nucleotide ; Psychometrics/instrumentation ; Psychometrics/methods ; Reproducibility of Results ; Risk Factors
    Language English
    Publishing date 2019-09-10
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1108742-0
    ISSN 1460-2083 ; 0964-6906
    ISSN (online) 1460-2083
    ISSN 0964-6906
    DOI 10.1093/hmg/ddz205
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  6. Article ; Online: Do we measure or compute polygenic risk scores? Why language matters.

    Penders, Bart / Janssens, A Cecile J W

    Human genetics

    2021  Volume 141, Issue 5, Page(s) 1093–1097

    Abstract: Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ... ...

    Abstract Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ways, blood flow has pressure and sodium is available in a concentration that can be quantified and visualised. In stark contrast, PRSs are calculated, compiled or constructed through the statistical assemblage of genetic variants. How researchers frame and name PRSs has consequences for how we interpret and value their results. We distinguish between the tangible and inferential understanding of PRS and the corresponding languages of measurement and computation, respectively. The conflation of these frames obscures important questions we need to ask: what PRS seeks to represent, whether current ways of 'doing PRS' are optimal and responsible, and upon what we base the credibility of PRS-based knowledge claims.
    MeSH term(s) Genome-Wide Association Study ; Humans ; Language ; Multifactorial Inheritance/genetics ; Risk Factors ; Sodium
    Chemical Substances Sodium (9NEZ333N27)
    Language English
    Publishing date 2021-02-15
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 223009-4
    ISSN 1432-1203 ; 0340-6717
    ISSN (online) 1432-1203
    ISSN 0340-6717
    DOI 10.1007/s00439-021-02262-7
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  7. Article ; Online: Designing babies through gene editing: science or science fiction?

    Janssens, A Cecile J W

    Genetics in medicine : official journal of the American College of Medical Genetics

    2016  Volume 18, Issue 12, Page(s) 1186–1187

    MeSH term(s) Bioethical Issues ; Bioethics ; Blastocyst/metabolism ; CRISPR-Cas Systems ; Cellular Reprogramming/genetics ; Embryo Research/ethics ; Female ; Gene Editing/ethics ; Gene Editing/methods ; Gene Editing/statistics & numerical data ; Humans ; Infant, Newborn ; Mutagenesis/ethics ; Mutagenesis/physiology ; Pregnancy ; Prenatal Diagnosis
    Language English
    Publishing date 2016-04-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1455352-1
    ISSN 1530-0366 ; 1098-3600
    ISSN (online) 1530-0366
    ISSN 1098-3600
    DOI 10.1038/gim.2016.28
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  8. Article ; Online: Critical examination of current response shift methods and proposal for advancing new methods.

    Sébille, Véronique / Lix, Lisa M / Ayilara, Olawale F / Sajobi, Tolulope T / Janssens, A Cecile J W / Sawatzky, Richard / Sprangers, Mirjam A G / Verdam, Mathilde G E

    Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation

    2021  Volume 30, Issue 12, Page(s) 3325–3342

    Abstract: Purpose: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally ...

    Abstract Purpose: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods.
    Methods: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified.
    Results: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points.
    Conclusion: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward.
    MeSH term(s) Computer Simulation ; Humans ; Models, Theoretical ; Quality of Life/psychology ; Research Design
    Language English
    Publishing date 2021-02-17
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1161148-0
    ISSN 1573-2649 ; 0962-9343
    ISSN (online) 1573-2649
    ISSN 0962-9343
    DOI 10.1007/s11136-020-02755-4
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  9. Article ; Online: Reflection on modern methods: Revisiting the area under the ROC Curve.

    Janssens, A Cecile J W / Martens, Forike K

    International journal of epidemiology

    2020  Volume 49, Issue 4, Page(s) 1397–1403

    Abstract: The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive ... ...

    Abstract The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.
    MeSH term(s) Area Under Curve ; ROC Curve
    Language English
    Publishing date 2020-01-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 187909-1
    ISSN 1464-3685 ; 0300-5771
    ISSN (online) 1464-3685
    ISSN 0300-5771
    DOI 10.1093/ije/dyz274
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  10. Article ; Online: The hidden harm behind the return of results from personal genome services: a need for rigorous and responsible evaluation.

    Janssens, A Cecile J W

    Genetics in medicine : official journal of the American College of Medical Genetics

    2014  Volume 17, Issue 8, Page(s) 621–622

    MeSH term(s) Anxiety/etiology ; Depression/etiology ; Genetic Testing ; Humans ; Public Health Surveillance ; Truth Disclosure
    Language English
    Publishing date 2014-11-20
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1455352-1
    ISSN 1530-0366 ; 1098-3600
    ISSN (online) 1530-0366
    ISSN 1098-3600
    DOI 10.1038/gim.2014.169
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