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Related Concept Videos

Variance01:15

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Robust versus consistent variance estimators in marginal structural Cox models.

Dirk Enders1, Susanne Engel2, Roland Linder2

  • 1Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.

Statistics in Medicine
|June 12, 2018
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Summary
This summary is machine-generated.

The robust variance estimator is generally preferred over the consistent estimator for marginal structural models. While both are used in survival analysis, the robust estimator provides more reliable confidence intervals in practical applications, especially with smaller datasets.

Keywords:
inverse-probability-of-censoringinverse-probability-of-treatmentsimulation studysurvival analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Inverse-probability-of-treatment (IPT) and inverse-probability-of-censoring (IPC) weighted estimators are crucial for marginal structural models in survival analysis.
  • These models address time-dependent confounding and censoring, common challenges in observational studies.
  • Current practice often uses a robust variance estimator, which can lead to conservative confidence intervals.

Purpose of the Study:

  • To provide a detailed derivation of the variance for the IPT/IPC weighted estimator.
  • To explicitly state terms needed for a consistent variance estimator.
  • To compare the performance of robust and consistent variance estimators.

Main Methods:

  • Detailed mathematical derivation of the asymptotic variance distribution for IPT/IPC weighted estimators.
  • Application of both robust and consistent variance estimators to routine health care data.
  • A simulation study evaluating estimator performance across various data sizes and confounding scenarios.

Main Results:

  • No substantial differences between robust and consistent estimators were found in medium to large datasets without unmeasured confounding.
  • The consistent variance estimator showed poor performance in small samples or with unmeasured confounding and numerous confounders.
  • The robust variance estimator yielded conservative but generally more appropriate confidence intervals across tested scenarios.

Conclusions:

  • The robust variance estimator is recommended for practical applications in survival analysis using marginal structural models.
  • The consistent variance estimator's limitations in small samples or under unmeasured confounding necessitate caution.
  • Further research may be needed to improve the implementation and performance of consistent variance estimators.