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Bootstrap inference when using multiple imputation.

Michael Schomaker1, Christian Heumann2

  • 1Centre for Infectious Disease Epidemiology & Research, University of Cape Town, Falmouth Building, Observatory, Cape Town, 7925, South Africa.

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|April 24, 2018
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Summary
This summary is machine-generated.

This study introduces four methods combining multiple imputation and bootstrapping for valid statistical inference with missing data. Three methods proved effective, with performance varying by imputation set number and missing data extent.

Keywords:
HIVcausal inferenceg-methodsmissing dataresampling

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Many statistical estimators need bootstrapping for confidence intervals due to unavailable analytic standard errors or nonsymmetric parameter distributions.
  • Addressing missing data with multiple imputation presents challenges for valid bootstrap inference.

Purpose of the Study:

  • To present and evaluate four methods for combining bootstrap estimation with multiple imputation.
  • To determine which methods provide valid statistical inference in the presence of missing data.

Main Methods:

  • Development of four novel methods integrating bootstrap estimation and multiple imputation.
  • Simulation studies to assess method performance with varying numbers of imputed datasets and missing data extents.
  • Application to HIV treatment research using the g-formula for optimal antiretroviral treatment initiation timing.

Main Results:

  • Three of the four proposed methods yield valid statistical inference.
  • Method performance is influenced by the number of imputed datasets and the degree of missingness.
  • The g-formula analysis in HIV research demonstrates practical implications for complex missing data scenarios.

Conclusions:

  • The study provides practical, implementable solutions for valid bootstrap inference with multiple imputation.
  • Careful consideration of method choice is necessary based on data characteristics and missingness levels.
  • The findings have significant implications for statistical analysis in fields with complex missing data, such as HIV research.