Health Policy Statistics Section Student Paper Competition

Precision medicine aims to estimate heterogeneous treatment effects (HTE) that vary among individuals. However, a notable research gap exists in the estimation of HTE in the observational studies with survival outcomes. This paper proposes a comprehensive methodology for analyzing HTE in survival data, including a pseudo-outcome framework that generates six meta-learners to estimate HTE, a variable importance metric to identify predictive variables, and a data-adaptive procedure to select beneficial subgroups. Finite sample performance is evaluated in various observational study settings. We further analyzed the heterogeneous effects of a written asthma action plan (WAAP) on time-to-ED (Emergency Department) return for asthma exacerbation, using a large EHR dataset spanning pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients but with enhanced benefit from WAAP during the pandemic and depicted patient profiles. Our study offers valuable insights for healthcare policymakers and providers in advocating influenza vaccination and strategic WAAP distribution to particularly vulnerable groups during disruptive events, ultimately enhancing pediatric asthma care. 

The effects of continuous treatments are often characterized through the average dose response function, which is challenging to estimate from observational data due to confounding and positivity violations. Modified treatment policies (MTPs) are an alternative approach that aim to assess the effect of a modification to observed treatment values and work under relaxed assumptions. Estimators for MTPs generally focus on estimating the conditional density of treatment given covariates and using it to construct weights. However, weighting using conditional density models has well-documented challenges. This paper investigates the role of weights for MTPs showing that to control confounding, weights should balance the weighted data to an unobserved hypothetical target population that can be characterized with observed data. Leveraging this insight, we present a versatile set of tools to enhance estimation for MTPs. We introduce a distance that measures imbalance of covariate distributions under the MTP and use it to develop new weighting methods and tools to aid in the estimation of MTPs. We illustrate our methods through an example studying the effect of mechanical power. 

To effectively monitor long-term patient outcomes, it is critical to assess the dynamic risk of prognosis. This often involves utilizing multiple data sources (e.g., tumor registries, treatment histories, and patient-reported outcomes). However, challenges arise in selecting predictive features for patient outcomes from high-dimensional data, aligning longitudinal measurements from multiple sources, and summarizing model performance. We develop the penalized landmark supermodel (penLM) for dynamic risk prediction with high-dimensional, potentially multi-source data and novel metrics that summarize model performance (AUC or Brier Score) across several time points by incorporating temporal correlations. Through simulations, we assessed the coverage of the novel metrics' confidence intervals and the tests' power and type I error. We applied penLM to predict the updated 5-year risk of lung cancer mortality at diagnosis and for subsequent years by combining data from SEER registries, Medicare insurance claims, Medicare Health Outcome Survey, and the U.S. Census, revealing superior predictive accuracy compared to single-source models. The framework is available in our R package, dynamicLM. 

Gun violence is a major problem in contemporary American society. However, relatively little is known about the effects of firearm injuries on survivors and their family members and how these effects vary across subpopulations. To study these questions and, more generally, to address a gap in the methodological causal inference literature, we present a framework for the study of effect modification or heterogeneous treatment effects in difference-in-differences designs. We implement a new matching technique, combining profile matching and risk set matching, to (i) preserve the time alignment of covariates, exposure, and outcomes, avoiding pitfalls of other common approaches for difference-in-differences, and (ii) explicitly control biases due to imbalances in observed covariates in subgroups discovered from the data. Our case study shows significant and persistent effects of nonfatal firearm injuries on several health outcomes for those injured and on the mental health of their family members. The effects for families are strongest for those whose relative's injury is documented as resulting from an assault, self-harm, or law enforcement intervention. 

Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While established methodologies exist for estimating effects under binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. Here, we propose innovative estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of treatment, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the diverse effects of a nutritional excise tax. We then introduce methodological extensions to transport heterogeneous effects to new environments.