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Marginal analysis of incomplete longitudinal binary data: a cautionary note on LOCF imputation.

Richard J Cook1, Leilei Zeng, Grace Y Yi

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The last observation carried forward (LOCF) method for incomplete longitudinal data in clinical trials can cause significant bias and inflated error rates. Alternative analyses using all available data offer more reliable treatment effect estimation.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Longitudinal Data Analysis

Background:

  • Incomplete data is common in longitudinal studies, especially pharmaceutical trials.
  • The last observation carried forward (LOCF) is a widely used imputation method for missing data.
  • Existing methods for handling missing data have seen limited adoption in practice.

Purpose of the Study:

  • To evaluate the performance of the LOCF imputation strategy for longitudinal binary data.
  • To assess bias, type I error rates, and coverage probability of LOCF-based estimators and tests.
  • To compare LOCF with alternative methods using all available data and inverse probability weighting.

Main Methods:

  • Analysis of asymptotic and empirical bias, type I error, and coverage probability.
  • Application to longitudinal binary data using generalized estimating equations.
  • Comparison with analyses based on final follow-up response and inverse probability weighted methods.

Main Results:

  • LOCF imputation leads to substantial bias in treatment effect estimators.
  • Type I error rates are greatly inflated, and coverage probabilities deviate from nominal levels with LOCF.
  • Alternative analyses using all available data show smaller bias; inverse probability weighting provides consistent estimators when correctly specified.

Conclusions:

  • The LOCF method is unreliable for analyzing incomplete longitudinal data in pharmaceutical trials.
  • Alternative methods utilizing all available data or inverse probability weighting are superior to LOCF.
  • Proper handling of missing data is crucial for accurate estimation of treatment effects.