Martha Barnard

Wearable devices can accurately measure human behavior, providing a unique opportunity to understand how behavior impacts health. Recent studies leveraging functional regression methods have found a strong relationship between accelerometer-collected physical activity and mortality. However, to determine if physical activity patterns impact mortality it is necessary to understand the causal effects of policies for physical activity, i.e., a function-valued treatment. Functional treatments present several challenges for causal effect estimation: 1) defining a scientifically meaningful estimand that reflects real-world policies and satisfies positivity is nontrivial; and 2) the potential for temporal confounding over continuous time. To address these, we propose stochastic policies for functional treatments that allow estimation of causal effects of changing the treatment distribution without requiring a positivity assumption. We develop a novel method for such that modifies the treatment through a single basis function chosen by the analyst, allowing for clear control over treatment modification and temporal confounding feedback. We show asymptotic normality of our estimators and that they exhibit rate double robustness. We apply our methods to the National Health and Nutrition Examination Survey to determine the causal effect of increasing physical activity over three-hour periods on mortality.

Estimating the causal effect of a treatment or health policy with observational data can be challenging due to an imbalance of and a lack of overlap between treated and control covariate distributions. In the presence of limited overlap, researchers choose between (1) methods (e.g., inverse probability weighting) that imply traditional estimands but whose estimators are at risk of considerable bias and variance; and (2) methods (e.g., overlap weighting) which imply a different estimand by modifying the target population to reduce variance. We propose a framework for navigating the tradeoffs between variance and bias due to imbalance and a lack of overlap and the targeting of the estimand of scientific interest. We introduce a bias decomposition that encapsulates bias due to (1) the statistical bias of the estimator; and (2) estimand mismatch, i.e., deviation from the population of interest. We propose two design-based metrics and an estimand selection procedure that help illustrate the tradeoffs between these sources of bias and variance of the resulting estimators. Our procedure allows analysts to incorporate their domain-specific preference for preservation of the original research population versus reduction of statistical bias. We demonstrate how to select an estimand based on these preferences with an application to right heart catheterization data.

Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to cluster the data without the calculation of a distance matrix. Our technique is substantially faster than conventional methods based on computing pairwise distances via sequence alignment algorithms and also enhances interpretability of results. We compare our method to distance-based hierarchical clustering and nTreeClus through simulation studies and an application to data collected by Daynamica, an app that turns sensor data into a daily summary of a user’s activities. Among days that contain a large portion of time spent at home, our method distinguishes days that also contain multiple hours of travel or other activities, while both comparison methods fail to identify these patterns. We further identify which day patterns classified by our method are associated with higher concern for contracting COVID-19 with implications for public health messaging. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Positivity violations pose significant challenges for causal effect estimation with observational data. Under positivity violations, available methods result in either treatment effect estimators with substantial statistical bias and variance or estimators corresponding to a modified estimand and target population that is misaligned with the original research question. To address these challenges, we propose partially retargeted balancing weights, which yield reduced estimator variance under positivity violations by modifying the target population for only a subset of covariates. Our weights can be derived under a novel relaxed positivity assumption allowing the calculation of valid balancing weights even when positivity does not hold. Our proposed weighted estimator is consistent for the original target estimand when either 1) the implied propensity score model is correct; or 2) the subset of covariates whose population is not modified contains all treatment effect modifiers. When these conditions do not hold, our estimator is consistent for a slightly modified treatment effect estimand. Furthermore, our proposed weighted estimator has reduced asymptotic variance when positivity does not hold. We evaluate our weights and corresponding estimator through applications to synthetic data, an EHR study, and when transporting an RCT treatment effect to a Midwestern population.