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Reflection on modern methods: combining weights for confounding and missing data.

Rachael K Ross1, Alexander Breskin1,2, Tiffany L Breger1,3

  • 1Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA.

International Journal of Epidemiology
|September 18, 2021
PubMed
Summary
This summary is machine-generated.

This study explores using combined inverse probability weights to address multiple biases like confounding and missing data in epidemiological studies. It details methods for constructing these weights, especially when confounder data is missing.

Keywords:
Inverse probability weightsconfoundingmissing data

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Inverse probability weights are common for single biases in epidemiology.
  • Combined weights for multiple biases are mainly discussed for longitudinal marginal structural models.
  • Existing literature lacks comprehensive guidance on combined weights for confounding and missingness in time-fixed settings.

Purpose of the Study:

  • To examine combined inverse probability weights for confounding and missingness in time-fixed epidemiological settings.
  • To discuss identification conditions and construction methods for these combined weights.
  • To investigate the impact of missing data mechanisms on weight construction.

Main Methods:

  • Examined two examples of combined weights for confounding and missingness.
  • Discussed identification conditions and construction of combined weights.
  • Utilized simulation studies to illustrate weight estimation and application.
  • Analyzed the impact of missing data mechanisms on weight construction.

Main Results:

  • Construction of combined weights is straightforward when only outcome data are missing.
  • When confounder data are missing, a specific sequential estimation procedure is generally required.
  • Treatment probabilities must be estimated after applying missingness weights if confounder data is missing.
  • Conditional independence between treatment and missingness allows estimation among complete cases.

Conclusions:

  • Combined inverse probability weights can address multiple biases in time-fixed epidemiological analyses.
  • The presence and type of missing data (outcome vs. confounder) dictate the complexity of weight construction.
  • Specific estimation procedures are necessary when confounder data are missing to ensure valid causal inference.