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Combining propensity score-weighting and multiple imputation is complex but achievable. This tutorial details methods for valid inference in epidemiological research, using post-traumatic stress disorder in Syrian refugees as a case study.

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Area of Science:

  • Epidemiological research
  • Statistical methodology
  • Public health

Background:

  • Propensity score-weighting and multiple imputation are standard methods for controlling confounders and handling missing data in epidemiology.
  • Combining these techniques requires careful consideration of assumptions and practical implementation for valid statistical inference.
  • The tutorial addresses the complexities of integrating these methods within a real-world research context.

Purpose of the Study:

  • To outline the assumptions and considerations for combining propensity score-weighting with multiple imputation.
  • To provide a practical guide for researchers on implementing these combined methods.
  • To illustrate the application of these methods using a study on post-traumatic stress disorder in Syrian refugees.

Main Methods:

  • Logistic regression-based propensity scores were used to generate "standardized mortality ratio"-weights.
  • Substantive Model Compatible-Full Conditional Specification (SMC-FCS) was employed for multiple imputation of missing data.
  • A percentile confidence interval was calculated using bootstrapping with 999 estimates.

Main Results:

  • A propensity score model with weight truncation achieved acceptable covariate balance.
  • Modifications to the propensity score model were made for multiple imputation due to computational constraints.
  • The SMC-FCS imputation utilized regression models for partially observed covariates with specified imputation and iteration counts.

Conclusions:

  • Combining propensity score-weighting and multiple imputation presents methodological and computational challenges.
  • The process demands significant workload and computational time but clarifies underlying assumptions.
  • Despite challenges, the integration is feasible and essential for robust epidemiological analysis, with computational demands expected to decrease with technological advancements.