Combining propensity score‐based stratification and weighting to improve causal inference in the evaluation of health care interventions

When a randomized controlled trial is not feasible, a key strategy in observational studies is to ensure that intervention and control groups are comparable on observed characteristics and assume that the remaining unmeasured characteristics will not bias the results. In the past few years, propensity score-based techniques such as matching, stratification and weighting have become increasingly popular for evaluating health care interventions. Recently, marginal mean weighting through stratification (MMWS) has been introduced as a flexible pre-processing approach that combines the salient features of propensity score stratification and weighting to remove imbalances of pre-intervention characteristics between two or more groups under study. The weight is then used within the appropriate outcome model to provide unbiased estimates of treatment effects. In this paper, the MMWS technique is introduced by illustrating its implementation in three typical experimental conditions: a binary treatment (treatment versus control), an ordinal level treatment (varying doses) and nominal treatments (multiple independent arms). These methods are demonstrated in the context of health care evaluations by examining the pre-post difference in hospitalizations following the implementation of a disease management program for patients with congestive heart failure. Because of the flexibility and wide application of MMWS, it should be considered as an alternative procedure for use with observational data to evaluate the effectiveness of health care interventions.