6  Multilevel modelling

Relatively unexplored area of research.

• Li, Zaslavsky, and Landrum (2013) showed that “exploiting the multilevel structure in at least one stage can greatly reduce the bias”. They emphasize that propensity score methods offer a more robust alternative to regression adjustment, especially in complex multilevel observational data where correctly specifying the outcome model may be challenging.

• However, to properly estimate standard error for the treatment effect estimate from a multi-level or complex survey (where clustering and or stratification are present), it is necessary to address clustering options through outcome modelling (Austin, Jembere, and Chiu 2018).

Austin, Peter C, Nathaniel Jembere, and Maria Chiu. 2018. “Propensity Score Matching and Complex Surveys.” Statistical Methods in Medical Research 27 (4): 1240–57.

Li, Fan, Alan M Zaslavsky, and Mary Beth Landrum. 2013. “Propensity Score Weighting with Multilevel Data.” Statistics in Medicine 32 (19): 3373–87.