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Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy

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Abstract

We propose new time-dependent sensitivity, specificity, ROC curves and net reclassification indices that can take into account biomarkers or scores that are repeatedly measured at different time-points. Inference proceeds through inverse probability weighting and resampling. The newly proposed measures exploit the information contained in biomarkers measured at different visits, rather than using only the measurements at the first visits. The contribution is illustrated via simulations and an original application on patients affected by dilated cardiomiopathy. The aim is to evaluate if repeated binary measurements of right ventricular dysfunction bring additive prognostic information on mortality/urgent heart transplant. It is shown that taking into account the trajectory of the new biomarker improves risk classification, while the first measurement alone might not be sufficiently informative. The methods are implemented in an R package (longROC), freely available on CRAN.

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Acknowledgements

The authors are grateful to an AE and two referees for kind comments that helped improve the presentation.

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Authors and Affiliations

  1. Dipartimento Universitario Clinico di Scienze Mediche Chirurgiche e della Salute, Università di Trieste, Strada di Fiume, 447, 34149, Trieste, Italy

    Giulia Barbati

  2. Dipartimento di Sanità Pubblica e Malattie Infettive, Università di Roma “La Sapienza”, Piazzale Aldo Moro, 5, 00185, Rome, Italy

    Alessio Farcomeni

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  1. Giulia Barbati
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Correspondence to Alessio Farcomeni.

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Barbati, G., Farcomeni, A. Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy. Stat Methods Appl 27, 545–557 (2018). https://doi.org/10.1007/s10260-017-0410-2

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  • DOI: https://doi.org/10.1007/s10260-017-0410-2

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