Nonparametric estimation of marginal distributions under bivariate truncation with application to testing for age-of-onset anticipation

Jian Huang, Veronica J. Vieland, Kai Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Bivariate truncated data arise from the study of age-of-onset anticipation for diseases with variable age of onset in which children tend to develop clinical disease at younger ages than their affected parents. To test for age-of-onset anticipation using affected parent-child pair data, it is of interest to estimate the marginal distributions of the age-of-onset for both parents and children. However, the observed ages of onset in both parents and children are right-truncated by their current ages. In this report, we proposed a nonparametric estimator of the marginal distributions of a bivariate distribution based on right-truncated data. This estimator is shown to be consistent under appropriate conditions. A noniterative algorithm is given to compute the proposed estimator. Finite sample behavior of the estimator is investigated via simulation. An example is given to illustrate the use of the proposed estimator in testing of age-of-onset anticipation in bipolar affective disorder.
Original languageEnglish
Pages (from-to)1047-1068
Number of pages22
JournalStatistica Sinica
Volume11
Issue number4
Publication statusPublished - 1 Oct 2001
Externally publishedYes

Keywords

  • Age of onset
  • Anticipation
  • Bivariate data
  • Consistency
  • Marginal distribution
  • Truncation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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