Matching in Surveys

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Suggestions PS Model Outcome Model Source
PS stratification No weights Use weights
(no comment on strata / cluster)
Zanutto, E. L. (2006)
PS as covariate Use weights + strata + cluster as predictors Use weights + strata + cluster as design features DuGoff, E. H., Schuler, M., & Stuart, E. A. (2014)
Weighting Use weights (+ strata + cluster) as design features Use weights (+ strata + cluster) as design features Ridgeway, G., Kovalchik, S. A., Griffin, B. A., & Kabeto, M. U. (2015)
Matching Use original weights + strata + cluster as design features Use weights + strata + cluster as design features Austin, P. C., Jembere, N., & Chiu, M. (2018)
Matching Balance should be deciding factor Use weights + strata + cluster as design features Lenis, D., Nguyen, T. Q., Dong, N., & Stuart, E. A. (2017)

Reasonable approach (my suggestion; similar to Lenis et al. 2017):

  • Use if it provides best balance (e.g., measured by SMD) for PS model building. Often applying design features in the PS estimation is not possible due to the shortcoming of the software available for the machine learning approach chosen to estimate PS.
  • Must use all design features in the outcome modelling for population estimates |

References (Optional)

  1. Zanutto, E. L. (2006). A comparison of propensity score and linear regression analysis of complex survey data. Journal of data Science, 4(1), 67-91.
  2. DuGoff, E. H., Schuler, M., & Stuart, E. A. (2014). Generalizing observational study results: applying propensity score methods to complex surveys. Health services research, 49(1), 284-303.
  3. Lenis, D., Nguyen, T. Q., Dong, N., & Stuart, E. A. (2017). It’s all about balance: propensity score matching in the context of complex survey data. Biostatistics. 2019 Jan; 20(1): 147–163.
  4. Austin, P. C., Jembere, N., & Chiu, M. (2018). Propensity score matching and complex surveys. Statistical methods in medical research, 27(4), 1240-1257.