Dynamic landmark prediction for mixture data.

Dynamic landmark prediction for mixture data.

Publication date: Nov 23, 2019

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.

Garcia, T.P. and Parast, L. Dynamic landmark prediction for mixture data. 06791. 2019 Biostatistics.

Concepts Keywords
Biostatistics Estimator
Cohort Studies Estimation theory
Covariate Efficiency
Genetic Mutation


Type Source Name
disease MESH death
disease MESH Huntington disease
pathway KEGG Huntington disease


Original Article

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