Nonparametric modeling and analysis of association between Huntington's disease onset and CAG repeats

Dynamic landmark prediction for mixture data

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.

Statistical modeling of Huntington disease onset

Huntington disease (HD) is caused by a CAG trinucleotide expansion in the huntingtin gene. We now have the power to predict age-at-onset from subject-specific features like motor and neuroimaging measures. In clinical trials, properly modeling onset age is important, because it improves power calculations and directs clinicians to recruit subjects with certain features. The history of modeling onset, from simple linear and logistic regression to advanced survival models, is discussed. We highlight their advantages and disadvantages, emphasizing the methodological challenges when genetic mutation status is unavailable. We also discuss the potential bias and higher variability incurred from the uncertainty associated with subjective definitions for onset. Methods to adjust for the uncertainty in survival models are still in their infancy, but would be beneficial for HD and neurodegenerative diseases with long prodromal periods like Alzheimer's and Parkinson's disease.