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Related Experiment Videos

Modeling hazard functions in families

K Siegmund1, B McKnight

  • 1Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA.

Genetic Epidemiology
|April 29, 1998
PubMed
Summary
This summary is machine-generated.

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A genetic frailty model estimates cancer risk from family data. For familial breast cancer, it found a rare allele frequency (0.0009) and a high genetic relative risk (104).

Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Genetic susceptibility genes increase disease risk.
  • Accurate estimation of genetic parameters is crucial for understanding disease inheritance patterns.

Purpose of the Study:

  • To present a genetic frailty model for censored age of onset data in nuclear families.
  • To estimate genetic relative risk and allele frequency using maximum likelihood via the EM algorithm.
  • To address bias in parameter estimates when sampling from disease registries.

Main Methods:

  • Developed a genetic frailty model for censored age of onset data.
  • Employed maximum likelihood via the Expectation-Maximization (EM) algorithm.
  • Utilized likelihood corrections for biased samples from disease registries, approximating the full conditional likelihood.

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Main Results:

  • Simulations showed similar estimates for unbiased samples under parametric and semiparametric models.
  • For biased samples, the approximate conditional likelihood showed median estimates tending to under- and overestimate allele frequency and genetic relative risk, respectively.
  • The approximation was more accurate for rarer allele frequencies.
  • Applied to familial breast cancer data, the model estimated an allele frequency of 0.0009 and a genetic relative risk of 104.

Conclusions:

  • Maximum likelihood via EM algorithm is effective for estimating genetic parameters.
  • Likelihood corrections are essential for biased samples from disease registries.
  • The genetic frailty model, with appropriate corrections, can estimate significant genetic relative risk for diseases like familial breast cancer.