Determining the correct design and analysis of nonrandomized studies to estimate the effects of treatments is important in patient-centered outcomes research (PCOR). PCOR is meant to enable patients to make informed health care decisions based on their personal conditions, characteristics, and preferences. Increasingly, patients and physicians must choose from more than 2 treatment options. Methods based on propensity scores are popular for estimating causal effects of binary treatments from observational studies. Use of propensity score methods for more than 2 treatment options requires advanced techniques but has received limited attention in the literature.
This research consists of 2 objectives. The first is aimed at development, testing, and guidance for estimation of effects of multiple treatment options that are either ordinal (eg, ≥3 possible doses of a drug) or categorical (eg, ≥3 possible drugs). Specifically, we will concentrate on different matching procedures. In the second objective, we use the developed methods to estimate the effects of multiple add-on, noninsulin antihyperglycemic treatments on major adverse cardiovascular events (MACE) or death.
Methods based on the generalized propensity score (GPS), which relates to the probabilities of receiving each of the possible treatment options, have been proposed to address estimation of causal effects with more than 2 interventions. However, the relative benefits of different GPS models remain only partially identified, and the type of exposure (ordinal vs categorical) may influence this choice. Moreover, the identification of appropriate estimation methods (eg, weighting, matching) has been inadequately investigated. We develop the theoretical background for estimating causal effects in studies with multiple interventions. In addition, we propose new matching methods and use simulation analysis to compare their bias, variance, and mean squared error with currently used methods. For the second objective, we apply the newly developed methods to observational cohort data from the 2007-2015 Clinical Practice Research Datalink (CPRD).
We provide general guidelines and describe different statistical methods that can be used to estimate treatment effects with observational data when comparing multiple interventions. In the analysis of the CPRD data set, we found that using metformin plus sulfonylureas (gliclazide) increased the 3-year risk of MACE over metformin plus thiazolidinediones (pioglitazone). In addition, the former combination increased the 3-year risk of mortality over metformin plus dipeptidyl peptidase-4 inhibitors (sitagliptin) and metformin plus thiazolidinediones.
First, this study showed that simple extensions of procedures aimed at estimating the causal effects with binary treatment may produce misleading results when applied to the multiple-treatment setting. Special attention should be given to the different assumptions being made. Second, we developed multiple matching procedures and a multiple-imputation procedure. Among matching procedures, we found that matching on the Mahalanobis distance of the GPS with or without caliper provides the largest reduction in covariate bias. However, as the number of treatments increases, fuzzy matching provides the largest reduction in bias. Finally, we found that metformin plus gliclazide results in increased risk of MACE and mortality compared with metformin plus pioglitazone. In addition, metformin plus gliclazide results in increased risk of mortality compared with metformin plus sitagliptin.
All the procedures we examined were based on the assumption that the assignment mechanism is strongly unconfounded. When this assumption is violated, the causal estimates may be biased. Sensitivity analysis with multiple interventions is an area of future research.
