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Using Cox regression to develop linear rank tests with zero-inflated clustered data.

Stuart R Lipsitz1, Garrett M Fitzmaurice2, Debajyoti Sinha3

  • 1Brigham and Women's Hospital, Boston, MA, U.S.A.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|June 10, 2021
PubMed
Summary
This summary is machine-generated.

This study extends a two degree-of-freedom test for zero-inflated data to clustered settings. The new method uses generalized estimating equations (GEE) Cox models for improved efficiency in analyzing clustered zero-inflated outcomes.

Keywords:
General Social SurveyWilcoxon testgeneralized estimating equationslogrank testscore statistic

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical modeling

Background:

  • Zero-inflated data are common in health studies, presenting analytical challenges.
  • Existing methods for comparing two groups with independent zero-inflated data have limitations with clustered observations.

Purpose of the Study:

  • To extend a two degree-of-freedom statistical test for comparing zero-inflated data between two groups to clustered data settings.
  • To develop efficient statistical methods for handling zero-inflated outcomes in the presence of data clustering.

Main Methods:

  • Proposed a score statistic from a time-varying weighted Cox regression model with a robust sandwich variance estimator for clustered data.
  • Applied a generalized estimating equations (GEE) Cox model incorporating a non-independence working correlation to enhance efficiency.
  • Evaluated the proposed methods using a simulation study and real-world data from the General Social Survey on mental health days.

Main Results:

  • The proposed methods effectively extend the two degree-of-freedom test to clustered zero-inflated data.
  • The GEE Cox model demonstrated improved efficiency compared to naive independence assumptions.
  • The methods were successfully applied to analyze a zero-inflated outcome (days with mental health problems).

Conclusions:

  • The developed statistical approach provides a robust and efficient tool for analyzing clustered zero-inflated data.
  • This methodology enhances the ability to compare groups with complex zero-inflated outcomes in various research fields.
  • The study offers valuable statistical advancements for epidemiological and biostatistical research.