Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure
Bibliographic Data
| ID | 19291224 |
|---|---|
| Authors | Xiao Liu (0000-0001-6365-1191, The University of Texas at Austin, corresponding author) |
| Year | 2024 |
| Volume | 59 |
| Issue | 3 |
| Pages | 411-433 |
| Publication date | 2024-05-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2024.2307529 |
| PMID | 38379305 |
| OpenAlex | W4392013983 |
| Language | EN |
| References cited | 93 |
Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration
Cluster analysis · Covariate · Data mining · Econometrics · Hierarchical clustering · Imputation (statistics) · Inverse probability weighting · Meta-analysis · Missing data · Multilevel model · Propensity score matching · Random effects model · Statistics · Weighting · Advanced Causal Inference Techniques · Computer Science · Mathematics · Medicine · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Statistical analysis with missing data
Analysis of Incomplete Multivariate Data
Introducing Multilevel Modeling
Marginal Structural Models and Causal Inference in Epidemiology
General Multi-Level Modeling with Sampling Weights
Multiple Imputation of Missing Data for Multilevel Models
How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
Randomization Analysis of Experimental Data
Intraclass Correlation Values for Planning Group-Randomized Trials in Education
Effects of Kindergarten Retention Policy on Children’s Cognitive Growth in Reading and Mathematics
Generalized Eta and Omega Squared Statistics
Variable Selection for Propensity Score Models
Average causal effects from nonrandomized studies
Evaluating Kindergarten Retention Policy
Stratification and weighting via the propensity score in estimation of causal treatment effects
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Bayesian Inference for Causal Effects
Inference and missing data
Some Practical Guidance for the Implementation of Propensity Score Matching
The central role of the propensity score in observational studies for causal effects
Estimating causal effects of treatments in randomized and nonrandomized studies.
Sufficient Sample Sizes for Multilevel Modeling
Matching Methods for Causal Inference
Mice
Statistics and Causal Inference
The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials
Multiple Imputation for Nonresponse in Surveys
An Investigation of Factored Regression Missing Data Methods for Multilevel Models with Cross-Level Interactions
Tuning Random Forests for Causal Inference under Cluster-Level Unmeasured Confounding
The Use of Propensity Scores for Nonrandomized Designs With Clustered Data
Causal Inference with Multilevel Data
A Systematic Review of Propensity Score Methods in the Social Sciences
The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability
An Evaluation of Weighting Methods Based on Propensity Scores to Reduce Selection Bias in Multilevel Observational Studies
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
| Highly cited | No |
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