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March 1, 1985Biometrics806 citations

Estimation of a Common Effect Parameter from Sparse Follow-Up Data

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SGSander GreenlandJRJames M. Robins

Key Points

  • To evaluate the performance and consistency of effect estimators for common risk ratios, risk differences, and rate ratios under sparse-data follow-up settings.
  • Analyzed the asymptotic properties of Mantel-Haenszel, maximum likelihood, and weighted least squares estimators under binomial sparse-data models.
  • Evaluated rate ratio and rate difference estimators under Poisson sparse-data models for consistency and asymptotic variance under the null hypothesis.
  • Derived variance estimators applicable across both sparse stratifications and large strata for all Mantel-Haenszel estimators.
  • Mantel-Haenszel risk ratio and risk difference estimators remain consistent in binomial sparse stratifications, whereas maximum likelihood and weighted least squares estimators are biased.
  • Under Poisson models, Mantel-Haenszel and maximum likelihood rate ratio estimators are consistent with equal asymptotic variances under the null, while weighted least squares estimators remain biased.
  • For rate differences, estimators using Mantel-Haenszel weighting maintain consistency in sparse data, alongside variance estimators consistent in both sparse-data and large-strata settings.

Abstract

Breslow (1981, Biometrika 68, 73-84) has shown that the Mantel-Haenszel odds ratio is a consistent estimator of a common odds ratio in sparse stratifications. For cohort studies, however, estimation of a common risk ratio or risk difference can be of greater interest. Under a binomial sparse-data model, the Mantel-Haenszel risk ratio and risk difference estimators are consistent in sparse stratifications, while the maximum likelihood and weighted least squares estimators are biased. Under Poisson sparse-data models, the Mantel-Haenszel and maximum likelihood rate ratio estimators have equal asymptotic variances under the null hypothesis and are consistent, while the weighted least squares estimators are again biased; similarly, of the common rate difference estimators the weighted least squares estimators are biased, while the estimator employing "Mantel-Haenszel" weights is consistent in sparse data. Variance estimators that are consistent in both sparse data and large strata can be derived for all the Mantel-Haenszel estimators.

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Cite This Study

Greenland et al. (1985) studied this question.

synapsesocial.com/papers/69d8948a52654bb436d195e8https://doi.org/10.2307/2530643
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