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Pattern mixture models for the analysis of repeated attempt designs.

Michael J Daniels1, Dan Jackson2, Wei Feng3

  • 1Department of Integrative Biology, Department of Statistics & Data Sciences, University of Texas, Austin, TX, 78712.

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This study introduces a new pattern mixture model for analyzing repeated measurement attempts in follow-up studies. This flexible approach offers a transparent alternative to traditional selection models for understanding missing data patterns.

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Nonignorable missingnessRepeated attempt modelSelection modelSensitivity analysis

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Missing Data Methods

Background:

  • Follow-up studies often involve multiple attempts to collect data after baseline.
  • Recording the success or failure of these attempts is crucial for assessing missing data assumptions.
  • Differences in measurement data may exist between subjects with varying resistance to providing data.

Purpose of the Study:

  • To present a novel pattern mixture approach for modeling data from repeated measurement attempts.
  • To offer a more flexible and transparent alternative to existing selection models for this type of data.
  • To facilitate sensitivity analysis in longitudinal studies with complex missing data.

Main Methods:

  • Development and application of a pattern mixture model.
  • Re-analysis of existing repeated attempt data from a previous trial.
  • Comparison with a previously employed selection model framework.

Main Results:

  • The proposed pattern mixture model demonstrates greater flexibility compared to selection models.
  • The new model offers improved transparency in parameter identifiability.
  • The approach allows for robust sensitivity analysis.

Conclusions:

  • The pattern mixture approach provides a fully viable and advantageous alternative to established selection models for repeated attempt data.
  • This method enhances the understanding and modeling of missing data in longitudinal research.
  • The model's transparency and flexibility support more reliable statistical inferences.