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Improving the Estimation of the Odds Ratio in Sampling Surveys using Auxiliary Information

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  • Goga, Camelia
  • Ruiz-Gazen, Anne

Abstract

The odds-ratio measure is widely used in Health and Social surveys where the aim is to compare the odds of a certain event between a population at risk and a population not at risk. It can be defined using logistic regression through an estimating equation that allows a generalization to continuous risk variable. Data from surveys need to be analyzed in a proper way by taking into account the survey weights. Because the odds-ratio is a complex parameter, the analyst has to circumvent some difficulties when estimating confidence intervals. The present paper suggests a nonparametric approach that can take advantage of some auxiliary information in order to improve on the precision of the odds-ratio estimator. The approach consists in B-spline modelling which can handle the nonlinear structure of the parameter in a flexible way and is easy to implement. The variance estimation issue is solved through a linearization approach and confidence intervals are derived. Two small illustrations are discussed.

Suggested Citation

  • Goga, Camelia & Ruiz-Gazen, Anne, 2019. "Improving the Estimation of the Odds Ratio in Sampling Surveys using Auxiliary Information," TSE Working Papers 19-1000, Toulouse School of Economics (TSE), revised Jul 2020.
    • Handle: RePEc:tse:wpaper:122890
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    References listed on IDEAS

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    1. Camelia Goga & Anne Ruiz-Gazen, 2014. "Efficient estimation of non-linear finite population parameters by using non-parametrics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 113-140, January.
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    More about this item

    Keywords

    B-spline functions; estimating equation; influence function; linearization; logistic regression; survey data;
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