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Award ID contains: 1737673

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  1. ; (, Journal of Computational and Graphical Statistics)
    null (Ed.)
    Full Text Available 
  2. ; (, Journal of Computational and Graphical Statistics)
    null (Ed.)
    Full Text Available 
  3. ; ; (, Proceedings of the 36th International Conference on Machine Learning)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Demography)
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  5. ; ; ; (, Biostatistics)
    null (Ed.)
    Summary In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature. 
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