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Last observation carried forward: a crystal ball?

Michael G Kenward1, Geert Molenberghs

  • 1Medical Statistics Unit, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Journal of Biopharmaceutical Statistics
|February 26, 2010
PubMed
Summary

Last observation carried forward (LOCF) and baseline observation carried forward (BOCF) are still popular for clinical trial data, despite criticism. These methods are incompatible with the assumption that dropout is independent of future outcomes.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Data Analysis

Background:

  • Incomplete clinical trial data present challenges in analysis.
  • Methods like last observation carried forward (LOCF), baseline observation carried forward (BOCF), and complete case analysis are frequently used.
  • More robust methods like direct-likelihood and direct-Bayesian analyses are available but less commonly implemented.

Purpose of the Study:

  • To critically evaluate the appropriateness of commonly used methods for handling missing data in clinical trials.
  • To highlight the theoretical limitations of LOCF and BOCF, particularly concerning the assumption of missingness mechanism independence.

Main Methods:

  • The study reviews existing criticisms of LOCF and BOCF.
  • It theoretically examines the compatibility of these methods with the assumption that the dropout mechanism is independent of unobserved future measurements, given observed data and covariates.

Main Results:

  • LOCF and BOCF face criticisms including potential bias (conservative or liberal) and violation of the intention-to-treat principle.
  • These methods are generally incompatible with the assumption of a missingness mechanism independent of future outcomes, except in rare, unrealistic scenarios.

Conclusions:

  • The widespread use of LOCF and BOCF for incomplete clinical trial data is questioned.
  • Their incompatibility with the missing data independence assumption poses a significant limitation for reliable data analysis in clinical trials.