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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Marginal structural models for multilevel clustered data.

Yujie Wu1, Benjamin Langworthy2, Molin Wang3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.

Biostatistics (Oxford, England)
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

Marginal structural models (MSMs) were enhanced to address clustered hearing data in the CHEARS study. New methods improve causal effect estimation for time-varying exposures, offering less biased and more efficient results.

Keywords:
Audiometric dataClustered dataMarginal structural modelsMeta-analysisMultilevel correlationWeighted GEE

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Marginal structural models (MSMs) are crucial for estimating causal effects of time-varying exposures with time-dependent confounders.
  • The Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA) presents data with a multilevel correlation structure (participants, time, testing sites).

Purpose of the Study:

  • To propose and evaluate novel methods for fitting MSMs that account for multilevel correlation structures in clustered data.
  • To assess the impact of different treatment probability modeling strategies on MSM parameter estimates, particularly with unmeasured confounders.
  • To apply the developed methods to real-world data for estimating the causal effect of aspirin use on hearing loss.

Main Methods:

  • Developed two methods for MSMs in clustered data: direct covariance modeling within weighted GEE and a two-stage approach using mixed-effects meta-analysis.
  • Conducted finite sample simulations to compare the performance of the proposed methods against standard approaches.
  • Investigated the influence of fixed- versus mixed-effects models for treatment probability estimation on MSM results.

Main Results:

  • The proposed methods yielded less biased and more efficient parameter estimates by effectively accounting for multilevel correlation.
  • Simulation results demonstrated the advantages of the new methods in handling complex data structures.
  • Application to the CHEARS AAA data provided causal effect estimates for aspirin use on hearing loss.

Conclusions:

  • The novel MSM fitting methods are effective for handling multilevel correlated data in epidemiological studies.
  • Accounting for multilevel structures is essential for accurate causal effect estimation in such settings.
  • The methods offer a robust approach for analyzing complex longitudinal data, as demonstrated in the CHEARS study.