The use of propensity scores to control for pretreatment imbalances on observed variables in non-randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade. For settings with two conditions of interest such as a treatment and a control, inverse probability of treatment weighted (IPTW) estimation with propensity scores estimated via boosted models has been shown in simulation studies to yield causal effect estimates with desirable properties. There are tools (e.g., the twang package in R) and guidance for implementing this method with two treatments. However, there is not such guidance for analyses of three or more treatments. The goals of this paper are two-fold: (1) to provide step-by-step guidance for researchers who want to implement propensity score weighting for multiple treatments and (2) to propose the use of generalized boosted models (GBM) for estimation of the necessary propensity score weights. We define the causal quantities that may be of interest to studies of multiple treatments and derive weighted estimators of those quantities. We present a detailed plan for using GBM to estimate propensity scores and using those scores to estimate weights and causal effects. Tools for assessing balance and overlap of pretreatment variables among treatment groups in the context of multiple treatments are also provided. A case study examining the effects of three treatment programs for adolescent substance abuse demonstrates the methods.

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic studies, because typically these studies support a wide range of analyses by many data users. Some of these analyses may involve complex modeling, including interactions and nonlinear relations. Identifying such relations and encoding them in imputation models, for example, in the conditional regressions for multiple imputation via chained equations, can be daunting tasks with large numbers of categorical and continuous variables. The authors present a nonparametric approach for implementing multiple imputation via chained equations by using sequential regression trees as the conditional models. This has the potential to capture complex relations with minimal tuning by the data imputer. Using simulations, the authors demonstrate that the method can result in more plausible imputations, and hence more reliable inferences, in complex settings than the naive application of standard sequential regression imputation techniques. They apply the approach to impute missing values in data on adverse birth outcomes with more than 100 clinical and survey variables. They evaluate the imputations using posterior predictive checks with several epidemiologic analyses of interest.

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IMPORTANCE Access to specialists such as dermatologists is often limited for Medicaid enrollees. Teledermatology has been promoted as a potential solution; however, its effect on access to care at the population level has rarely been assessed. OBJECTIVES To evaluate the effect of teledermatology on the number of Medicaid enrollees who received dermatology care and to describe which patients were most likely to be referred to teledermatology. DESIGN, SETTING, AND PARTICIPANTS Claims data from a large California Medicaid managed care plan that began offering teledermatology as a covered service in April 2012 were analyzed. The plan enrolled 382 801 patients in California's Central Valley, including 108 480 newly enrolled patients who obtained coverage after the implementation of the Affordable Care Act. Rates of dermatology visits by patients affiliated with primary care practices that referred patients to teledermatology and those that did not were compared.

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