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Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a

Andreas Staudt1,2, Jennis Freyer-Adam3,4, Till Ittermann5

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Summary
This summary is machine-generated.

Sensitivity analyses using latent growth modelling (LGM) assessed missing data in a brief alcohol intervention trial. Results showed no significant intervention effects, regardless of missing data assumptions, highlighting the importance of robust statistical methods.

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Area of Science:

  • Statistics
  • Psychology
  • Public Health

Background:

  • Missing data are common in randomized controlled trials (RCTs).
  • Sensitivity analyses for missing data mechanisms are recommended but rarely performed.
  • Latent growth modelling (LGM) can be used for sensitivity analyses in RCTs.

Purpose of the Study:

  • To demonstrate sensitivity analyses for different missing data mechanisms in RCTs using LGM.
  • To assess the impact of missing data assumptions on intervention efficacy conclusions.
  • To provide a statistical supplement for safeguarding research findings.

Main Methods:

  • Utilized data from a randomized controlled brief alcohol intervention trial with 1646 adults.
  • Employed a three-step LGM approach, including analysis of missing data patterns and logistic regression.
  • Implemented Diggle-Kenward selection, Wu-Carroll shared parameter, and pattern mixture models for non-ignorable missingness.

Main Results:

  • Both intervention and control groups reduced alcohol use over time.
  • No significant differences in intervention efficacy were found.
  • Conclusions regarding intervention efficacy remained consistent across different missing data assumptions (missing at random vs. missing not at random).

Conclusions:

  • The LGM approach effectively assesses the sensitivity of intervention efficacy conclusions to missing data assumptions.
  • The findings underscore the importance of conducting sensitivity analyses for non-ignorable missing data in RCTs.
  • This methodology serves as a valuable tool for researchers to validate their findings.