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Multiple imputation for non-monotone missing not at random data using the no self-censoring model.

Boyu Ren1,2, Stuart R Lipsitz3,4, Roger D Weiss2,5

  • 1Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA.

Statistical Methods in Medical Research
|August 30, 2023
PubMed
Summary
This summary is machine-generated.

Handling non-monotone missing data in longitudinal studies is challenging. This study introduces a novel multiple imputation method, the "no self-censoring" model, offering a robust alternative for analyzing complex missing data patterns in clinical trials.

Keywords:
Missing at randomfully conditional specificationmissing datasensitivity analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Existing methods for missing data in longitudinal studies are limited for non-monotone missingness patterns.
  • The standard 'missing at random' assumption may not accurately reflect realistic missing data processes in complex scenarios.

Purpose of the Study:

  • To propose and evaluate a novel multiple imputation approach for handling non-monotone missing data under the "no self-censoring" mechanism.
  • To investigate the performance of this new method for binary outcomes in longitudinal studies.

Main Methods:

  • Developed a multiple imputation approach based on the "no self-censoring" model.
  • Conducted simulation and asymptotic studies to assess the imputation method's performance.
  • Proposed a sensitivity analysis for departures from the "no self-censoring" assumption.

Main Results:

  • The proposed multiple imputation method demonstrates effective handling of non-monotone missing data.
  • Simulation results validate the performance of the "no self-censoring" imputation approach.
  • The study clarifies the relationship between "missing at random" and "no self-censoring" models.

Conclusions:

  • The "no self-censoring" multiple imputation offers a computationally efficient and statistically sound alternative to weighting methods for non-monotone missing data.
  • The method is applicable to binary outcomes and can be extended to non-binary data.
  • The approach was successfully illustrated in a substance use disorder clinical trial.