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Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.


ABSTRACT: We consider a situation where there is rich historical data available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors X, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y| X, B. The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We develop and evaluate several approaches for translating the external information into constraints on regression coefficients in a logistic regression model of Y| X, B. Borrowing from the measurement error literature we establish an approximate relationship between the regression coefficients in the models Pr(Y = 1| X , ?), Pr(Y = 1| X, B, ?) and E(B| X, ? ) for a Gaussian distribution of B. For binary B we propose an alternate expression. The simulation results comparing these methods indicate that historical information on Pr(Y = 1| X , ?) can improve the efficiency of estimation and enhance the predictive power in the regression model of interest Pr(Y = 1| X, B, ?). We illustrate our methodology by enhancing the High-grade Prostate Cancer Prevention Trial Risk Calculator, with two new biomarkers prostate cancer antigen 3 and TMPRSS2:ERG.

SUBMITTER: Cheng W 

PROVIDER: S-EPMC6519970 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.

Cheng Wenting W   Taylor Jeremy M G JMG   Gu Tian T   Tomlins Scott A SA   Mukherjee Bhramar B  

Journal of the Royal Statistical Society. Series C, Applied statistics 20180813 1


We consider a situation where there is rich historical data available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors <b>X</b>, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y<i>|</i> <b>X</b>, B. The additional variable B is a new biomarker, measured on a small number of  ...[more]

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