Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.

RATIONALE, AIMS AND
OBJECTIVES: Interventions with multivalued treatments are common in medical and health research; examples include comparing the efficacy of competing interventions and contrasting various doses of a drug. In recent years, there has been growing interest in the development of methods that estimate multivalued treatment effects using observational data. This paper extends a previously described analytic framework for evaluating binary treatments to studies involving multivalued treatments utilizing a machine learning algorithm called optimal discriminant analysis (ODA).
METHOD: We describe the differences between regression-based treatment effect estimators and effects estimated using the ODA framework. We then present an empirical example using data from an intervention including three study groups to compare corresponding effects.
RESULTS: The regression-based estimators produced statistically significant mean differences between the two intervention groups, and between one of the treatment groups and controls. In contrast, ODA was unable to discriminate between distributions of any of the three study groups.
CONCLUSIONS: Optimal discriminant analysis offers an appealing alternative to conventional regression-based models for estimating effects in multivalued treatment studies because of its insensitivity to skewed data and use of accuracy measures applicable to all prognostic analyses. If these analytic approaches produce consistent treatment effect P values, this bolsters confidence in the validity of the results. If the approaches produce conflicting treatment effect P values, as they do in our empirical example, the investigator should consider the ODA-derived estimates to be most robust, given that ODA uses permutation P values that require no distributional assumptions and are thus, always valid.