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Related Concept Videos

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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Published on: July 3, 2020

The relative performance of targeted maximum likelihood estimators.

Kristin E Porter1, Susan Gruber, Mark J van der Laan

  • 1University of California, Berkeley, CA, USA.

The International Journal of Biostatistics
|September 21, 2011
PubMed
Summary

Targeted maximum likelihood estimators (TMLEs) offer a robust solution for censored data challenges, particularly when dealing with positivity violations. TMLEs demonstrate superior performance compared to other methods in complex missing data scenarios.

Keywords:
censored datacollaborative double robustnesscollaborative targeted maximum likelihood estimationdouble robustestimator selectioninverse probability of censoring weightinglocally efficient estimationmaximum likelihood estimationsemiparametric modeltargeted maximum likelihood estimationtargeted minimum loss based estimationtargeted nuisance parameter estimator selection

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Censored data analysis involves ongoing debate regarding the performance of various statistical estimators.
  • Existing methods like maximum likelihood, IPCW-estimators, and double robust estimators face challenges, especially with positivity violations in missing data.
  • Prior research highlights the fragility of double robust and IPCW-estimators in simulations with positivity violations.

Purpose of the Study:

  • To introduce and evaluate Targeted Maximum Likelihood Estimators (TMLEs) for censored data.
  • To assess the performance of TMLEs in scenarios with positivity violations, a known challenge for other estimators.
  • To compare TMLEs against established methods using simulation studies.

Main Methods:

  • Development of Targeted Maximum Likelihood Estimators (TMLEs) that respect global bounds on continuous outcomes.
  • Simulation studies based on Kang and Schafer (2007) to evaluate estimator performance.
  • Modified simulations with increased estimation complexity to further challenge the estimators.

Main Results:

  • TMLEs demonstrate suitability for handling positivity violations in censored data.
  • TMLEs exhibit desirable properties including double robustness and semiparametric efficiency.
  • TMLEs perform practically well in simulations, outperforming other estimators in challenging scenarios.

Conclusions:

  • TMLEs provide a stable and efficient approach for estimating outcomes with censored and missing data.
  • The proposed TMLEs are particularly advantageous when dealing with positivity violations.
  • TMLEs offer a promising advancement in statistical methods for complex data structures.