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A limited dependent variable model for heritability estimation with non-random ascertained samples.

Timo M Bechger1, Dorret I Boomsma, Henk Koning

  • 1National Institute for Educational Measurement (Cito), Arnhem, The Netherlands. timo.bechger@citogroep.nl

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|May 31, 2002
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Summary
This summary is machine-generated.

This study introduces a statistical model to correct for sample selection bias in family asthma research. The method ensures that findings from a selected group accurately represent the broader population, improving asthma prevalence estimates.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Asthma research often faces challenges with non-random sample selection.
  • Volunteer bias can skew results when only a fraction of an initially selected sample participates in further studies.

Purpose of the Study:

  • To present a statistical model for correcting sample selection bias.
  • To enable representative population estimates from selected samples in family health studies.

Main Methods:

  • Developed a linear regression model (DeFries-Fulker) incorporating sample selection correction.
  • Utilized data from a Dutch family study on asthma and related symptoms.
  • Compared the proposed model with raw likelihood estimation, multiple imputation, and sample weighting.

Main Results:

  • The corrected model provides estimates representative of the original population.
  • Demonstrated the model's efficacy using a real-world asthma family dataset.
  • Results were compared against alternative statistical methods for bias correction.

Conclusions:

  • The proposed limited dependent variable model effectively addresses sample selection bias.
  • This approach enhances the generalizability of findings from selected study samples.
  • The method is particularly useful in epidemiological studies with volunteer bias.