A fully nonparametric estimator of the marginal survival function based on case-control clustered age-at-onset data

Malka Gorfine, Nadia Bordo, Li Hsu

Research output: Contribution to journalArticlepeer-review

Abstract

Consider a popular case-control family study where individuals with a disease under study (case probands) and individuals who do not have the disease (control probands) are randomly sampled from a well-defined population. Possibly right-censored age at onset and disease status are observed for both probands and their relatives. For example, case probands are men diagnosed with prostate cancer, control probands are men free of prostate cancer, and the prostate cancer history of the fathers of the probands is also collected. Inherited genetic susceptibility, shared environment, and common behavior lead to correlation among the outcomes within a family. In this article, a novel nonparametric estimator of the marginal survival function is provided. The estimator is defined in the presence of intra-cluster dependence, and is based on consistent smoothed kernel estimators of conditional survival functions. By simulation, it is shown that the proposed estimator performs very well in terms of bias. The utility of the estimator is illustrated by the analysis of case-control family data of early onset prostate cancer. To our knowledge, this is the first article that provides a fully nonparametric marginal survival estimator based on case-control clustered age-at-onset data.

Original languageEnglish
Pages (from-to)76-90
Number of pages15
JournalBiostatistics
Volume18
Issue number1
DOIs
StatePublished - 1 Jan 2017

Keywords

  • Case-control
  • Family study
  • Multivariate survival
  • Nonparametric estimator
  • Smoothed kernel estimator

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