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SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model.

Seong-Ho Lee1, Brian D Richardson2, Yanyuan Ma3

  • 1Department of Statistics, University of Seoul, South Korea.

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|March 25, 2026
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Summary

Researchers developed a new statistical method, SPARCC (SemiPArametric Robust estimation in a right-Censored Covariate model), to accurately model Huntington disease symptom progression before diagnosis using right-censored data.

Keywords:
Huntington diseasecensored covariatedoubly robustright-censoringsemiparametric efficient

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Area of Science:

  • Statistics
  • Biostatistics
  • Neurodegenerative disease research

Background:

  • Understanding pre-diagnostic symptom changes in Huntington disease is crucial.
  • Modeling symptom severity requires handling right-censored 'time of diagnosis' data.
  • Existing statistical methods have limitations in efficiency and robustness.

Purpose of the Study:

  • To develop a novel, robust, and efficient statistical estimator for analyzing right-censored covariate data.
  • To address limitations of current estimators in modeling pre-diagnostic symptom trajectories.
  • To introduce the SPARCC estimator and its associated R package for practical application.

Main Methods:

  • Proposed the SemiPArametric Robust estimation in a right-Censored Covariate model (SPARCC) estimator.
  • Demonstrated doubly robust properties when nuisance parameters are parametrically modeled.
  • Showed consistency and semiparametric efficiency with nonparametric or machine learning methods for nuisance parameters.

Main Results:

  • The SPARCC estimator demonstrates robustness and efficiency as theoretically predicted.
  • Empirical validation using the R package 'sparcc' confirms its claimed statistical properties.
  • Successful application to estimate Huntington disease symptom trajectories from Enroll-HD study data.

Conclusions:

  • The SPARCC estimator offers a statistically superior approach for analyzing pre-diagnostic Huntington disease symptom data.
  • The R package 'sparcc' provides a practical tool for researchers in this field.
  • This method enhances the understanding of disease progression in Huntington disease and potentially other neurodegenerative conditions.