When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records that are only just recently being leveraged to inform better treatment recommendations. These data present a unique challenge because they are high dimensional and observational. Our goal is to make personalized treatment recommendations based on the outcomes for past patients similar to a new patient. We propose and analyze 3 methods for estimating heterogeneous treatment effects using observational data. Our methods perform well in simulations using a wide variety of treatment effect functions, and we present results of applying the 2 most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.

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Acute sepsis-related neurologic dysfunction was the organ dysfunction most strongly associated with short- and long-term mortality and represents a key mediator of long-term adverse outcomes following sepsis.

There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies.

IMPORTANCE Among high-risk patients with hypertension, targeting a systolic blood pressure of 120 mm Hg reduces cardiovascular morbidity and mortality compared with a higher target. However, intensive blood pressure management incurs additional costs from treatment and from adverse events. OBJECTIVE To evaluate the incremental cost-effectiveness of intensive blood pressure management compared with standard management. DESIGN, SETTING, AND PARTICIPANTS This cost-effectiveness analysis conducted from September 2015 to August 2016 used a Markov cohort model to estimate cost-effectiveness of intensive blood pressure management among 68-year-old high-risk adults with hypertension but not diabetes. We used the Systolic Blood Pressure Intervention Trial (SPRINT) to estimate treatment effects and adverse event rates. We used Centers for Disease Control and Prevention Life Tables to project age- and cause-specific mortality, calibrated to rates reported in SPRINT. We also used population-based observational data to model development of heart failure, myocardial infarction, stroke, and subsequent mortality. Costs were based on published sources, Medicare data, and the National Inpatient Sample. INTERVENTIONS Treatment of hypertension to a systolic blood pressure goal of 120 mm Hg (intensive management) or 140 mm Hg (standard management). MAIN OUTCOMES AND MEASURES Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually. RESULTS Standard management yielded 9.6 QALYs and accrued $155 261 in lifetime costs, while intensive management yielded 10.5 QALYs and accrued $176 584 in costs. Intensive blood pressure management cost $23 777 per QALY gained. In a sensitivity analysis, serious adverse events would need to occur at 3 times the rate observed in SPRINT and be 3 times more common in the intensive management arm to prefer standard management. CONCLUSIONS AND RELEVANCE Intensive blood pressure management is cost-effective at typical thresholds for value in health care and remains so even with substantially higher adverse event rates.