Weighting methods are often used to generalize and transport estimates of causal effects from a study sample to a target population. Traditional methods construct the weights by separately modeling the treatment assignment and the study selection probabilities and then multiplying functions (e.g., inverses) of the estimated probabilities. These estimated multiplicative weights may not produce adequate covariate balance and can be highly variable, resulting in biased and/or unstable estimators, particularly when there is limited covariate overlap across populations or treatment groups. To address these limitations, we propose a weighting approach for both randomized and observational studies that weights each treatment group directly in 'one go' towards the target population. We present a general framework for generalization and transportation by characterizing the study and target populations in terms of generic probability distributions. Under this framework, we justify this one-step weighting approach. By construction, this approach directly balances covariates relative to the target population and produces weights that are stable. Moreover, in some settings, this approach does not require individual-level data from the target population. We connect this approach to inverse probability and inverse odds weighting. We show that the one-step weighting estimator for the target average treatment effect is consistent, asymptotically Normal, doubly-robust, and semiparametrically efficient. We demonstrate the performance of this approach using a simulation study and a randomized case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California.

We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible unweighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not always require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile comprises the characteristics of a single individual. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. The method can also be used for the selection of units for study follow-up, and it readily applies to multivalued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of the generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) the characteristics of a hypothetical vulnerable patient. The method can be implemented via the new function profmatch in the designmatch package for R, for which we provide a step-by-step tutorial.

We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible self-weighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the tasks of generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile can characterize a single patient. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. The method can also be used for the selection of units for study follow-up, and it readily applies to multi-valued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use treatment and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) a hypothetical vulnerable patient. We provide R code with step-by-step explanations to implement the methods in the paper in the Supplementary Materials.

No abstract

Micro-randomized trials (MRTs) are a novel experimental design for developing mobile health interventions. Participants are repeatedly randomized in an MRT, resulting in longitudinal data with time-varying treatments. Causal excursion effects are the main quantities of interest in MRT primary and secondary analyses. We consider MRTs where the proximal outcome is binary and the randomization probability is constant or time-varying but not data-dependent.We develop a sample size formula for detecting a nonzero marginal excursion effect. We prove that the formula guarantees power under a set of working assumptions. We demonstrate via simulation that violations of certain working assumptions do not affect the power, and for those that do, we point out the direction in which the power changes. We then propose practical guidelines for using the sample size formula. As an illustration, the formula is used to size an MRT on interventions for excessive drinking. The sample size calculator is implemented in R package MRTSampleSizeBinary and an interactive R Shiny app. This work can be used in trial planning for a wide range of MRTs with binary proximal outcomes.