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  1. Article ; Online: Why Is Modeling Coronavirus Disease 2019 So Difficult?

    Subramanian, Vigneshwar / Kattan, Michael W

    Chest

    2020  Volume 158, Issue 5, Page(s) 1829–1830

    MeSH term(s) Betacoronavirus ; COVID-19 ; Clinical Decision-Making ; Communicable Disease Control ; Coronavirus Infections/epidemiology ; Coronavirus Infections/mortality ; Coronavirus Infections/transmission ; Decision Making ; Forecasting ; Humans ; Models, Theoretical ; Pandemics ; Patient-Specific Modeling ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/mortality ; Pneumonia, Viral/transmission ; Risk Assessment ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-06-19
    Publishing country United States
    Document type Editorial
    ZDB-ID 1032552-9
    ISSN 1931-3543 ; 0012-3692
    ISSN (online) 1931-3543
    ISSN 0012-3692
    DOI 10.1016/j.chest.2020.06.014
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.MO 349: Show issues

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  2. Article ; Online: Developing a Clinical Prediction Score: Comparing Prediction Accuracy of Integer Scores to Statistical Regression Models.

    Subramanian, Vigneshwar / Mascha, Edward J / Kattan, Michael W

    Anesthesia and analgesia

    2021  Volume 132, Issue 6, Page(s) 1603–1613

    Abstract: Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, ... ...

    Abstract Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, therefore, investigated the impact on prediction accuracy when researchers simplify a regression model into an integer score using a simulation study and an example clinical data set. Simulated independent training and test sets (n = 1000) were randomly generated such that a logistic regression model would perform at a specified target area under the receiver operating characteristic curve (AUC) of 0.7, 0.8, or 0.9. After fitting a logistic regression with continuous covariates to each data set, continuous variables were dichotomized using data-dependent cut points. A logistic regression was refit, and the coefficients were scaled and rounded to create an integer score. A risk classification system was built by stratifying integer scores into low-, intermediate-, and high-risk tertiles. Discrimination and calibration were assessed by calculating the AUC and index of prediction accuracy (IPA) for each model. The optimism in performance between the training set and test set was calculated for both AUC and IPA. The logistic regression model using the continuous form of covariates outperformed all other models. In the simulation study, converting the logistic regression model to an integer score and subsequent risk classification system incurred an average decrease of 0.057-0.094 in AUC, and an absolute 6.2%-17.5% in IPA. The largest decrease in both AUC and IPA occurred in the dichotomization step. The dichotomization and risk stratification steps also increased the optimism of the resulting models, such that they appeared to be able to predict better than they actually would on new data. In the clinical data set, converting the logistic regression with continuous covariates to an integer score incurred a decrease in externally validated AUC of 0.06 and a decrease in externally validated IPA of 13%. Converting a regression model to an integer score decreases model performance considerably. Therefore, we recommend developing a regression model that incorporates all available information to make the most accurate predictions possible, and using the unaltered regression model when making predictions for individual patients. In all cases, researchers should be mindful that they correctly validate the specific model that is intended for clinical use.
    MeSH term(s) Area Under Curve ; Computer Simulation/statistics & numerical data ; Computer Simulation/trends ; Forecasting ; Humans ; Models, Statistical ; ROC Curve ; Regression Analysis ; Stroke/diagnosis ; Stroke/epidemiology
    Language English
    Publishing date 2021-01-15
    Publishing country United States
    Document type Comparative Study ; Journal Article
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000005362
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Prostate cancer: Numeracy and understanding of risk reduction of PSA screening.

    Subramanian, Vigneshwar / Kattan, Michael W

    Nature reviews. Urology

    2018  Volume 15, Issue 4, Page(s) 208–209

    MeSH term(s) Decision Making ; Early Detection of Cancer/methods ; Humans ; Male ; Mass Screening/methods ; Prostate-Specific Antigen/blood ; Prostatic Neoplasms/blood ; Prostatic Neoplasms/diagnosis ; Risk Assessment/methods
    Chemical Substances Prostate-Specific Antigen (EC 3.4.21.77)
    Language English
    Publishing date 2018-02-20
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2493737-X
    ISSN 1759-4820 ; 1759-4812
    ISSN (online) 1759-4820
    ISSN 1759-4812
    DOI 10.1038/nrurol.2018.21
    Database MEDical Literature Analysis and Retrieval System OnLINE

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