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More data, less information? Potential for nonmonotonic information growth using GEE.

Abigail B Shoben1, Kyle D Rudser2, Scott S Emerson3

  • 1a Division of Biostatistics , The Ohio State University , Columbus , Ohio , USA.

Journal of Biopharmaceutical Statistics
|April 7, 2016
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Statistical analysis of sequential data, like in clinical trials, can unexpectedly decrease information precision. This study shows how generalized estimating equations (GEE) can lead to nonmonotonic information growth under specific conditions.

Keywords:
Group sequential trialsinformation growthlongitudinal data

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Sequential data analysis, crucial for clinical trials, typically assumes increasing statistical information with more observations.
  • Generalized estimating equations (GEE) are commonly used for analyzing correlated data but may not guarantee monotonic information growth.

Purpose of the Study:

  • To demonstrate the theoretical possibility of nonmonotonic information growth when estimating slopes using GEE.
  • To provide intuition and identify conditions conducive to nonmonotonic information growth in statistical analyses.

Main Methods:

  • Theoretical derivations to establish the possibility of nonmonotonic information growth.
  • Simulation-based analyses to characterize specific scenarios leading to this phenomenon.
  • Focus on estimating a slope parameter within the GEE framework.

Main Results:

  • Nonmonotonic information growth is theoretically possible with GEE, contrary to typical statistical intuition.
  • This phenomenon is most likely when patient accrual is rapid, within-individual correlations are high, and measurement variability increases over time.

Conclusions:

  • Study designers must be aware of the potential for nonmonotonic information growth in sequential analyses using GEE.
  • Planning interim analyses is recommended to mitigate risks associated with nonmonotonic information growth in clinical trials.