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Related Experiment Video

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Rapid report on estimating incidence from cross-sectional data.

Justin B DeMonte1, Anne M Neilan2, Matthew S Loop1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.

Annals of Epidemiology
|September 26, 2020
PubMed
Summary

The maximum likelihood estimator (MLE) provides a more accurate incidence rate (IR) estimate from cross-sectional data than the crude estimator. The MLE is approximately unbiased, while the crude estimator significantly underestimates true incidence.

Keywords:
BiasCensored dataCross-sectionalEstimatorIncidence rateMaximum likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Incidence rate (IR) is commonly estimated using person-time at risk in prospective cohort studies.
  • This crude estimator is generally consistent for population IR under standard assumptions.

Purpose of the Study:

  • To evaluate incidence rate estimation using cross-sectional data when exact event times are unknown.
  • To compare the bias of the crude incidence rate estimator versus the maximum likelihood estimator (MLE).

Main Methods:

  • A simulation study was conducted to compare the bias of two estimators.
  • The maximum likelihood estimator (MLE) was investigated as an alternative to the crude estimator for cross-sectional data.

Main Results:

  • The crude estimator demonstrated a bias, consistently underestimating the true incidence.
  • The maximum likelihood estimator (MLE) was found to be approximately unbiased.
  • The bias of the crude estimator was substantially larger (one to two orders of magnitude) than that of the MLE.

Conclusions:

  • The maximum likelihood estimator (MLE) is a more accurate and straightforward method for calculating incidence rates from cross-sectional data.
  • The MLE is consistent when the underlying hazard rate is constant, offering a reliable alternative to the crude estimator.