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Rosenbaum and Rubin’s (1983) propensity score revolutionized the field of causal inference and has emerged as a standard tool when researchers reason about cause-and-effect relationship across many disciplines. This discussion centers around the key “no interference” assumption in Rosenbaum and Rubin’s original development of the propensity score and reviews some recent advances in extending the propensity score to studies involving dependent happenings.

The location of Plasmodium falciparum within the body is determined by the life cycle of the parasite; young rings are in the peripheral blood, whereas mature parasites are sequestered in deep tissues. We can calculate a “ring ratio,” the proportion of parasites in the periphery to the total number of parasites in the body. Artesunate acts on all parasite life stages, whereas quinine is effective only on sequestered parasites. Children with cerebral malaria (CM) treated with artesunate clear parasites faster than those treated with quinine. In this study, we established the relationship between ring ratio and parasite clearance rate and used the ring ratio to determine if the benefit derived from artesunate treatment could be attributed to its broader effect on life cycle stages. Ring ratios were calculated for 400 hospitalized children with CM in Blantyre, Malawi between 2010 and 2019 (quinine: 2010–2013, artesunate: 2014–2019). In both treatment groups, parasite clearance rates were positively associated with the ring ratios, with a stronger association in the artesunate era than the quinine era. In the quinine era, an increase of 1-unit log10 difference between parasitemia and plasma P falciparum histidine-rich protein 2 (a proxy for ring ratio) resulted in a 0.27-unit increase in the parasite clearance rate, whereas in the artesunate era an equal increase resulted in a 0.41-unit increase (P = .04 for the difference).This analysis provides in vivo evidence supporting the hypothesis that more rapid parasite clearance rates in artesunate recipients are due to its superiority over quinine in killing ring-stage parasites.

Intraoperative transesophageal echocardiography (TEE) is associated with improved outcomes after cardiac surgery, but unexplained practice pattern variation exists. This study aimed to identify and quantify the predictors of intraoperative TEE use among patients undergoing isolated coronary artery bypass graft surgery (CABG) or cardiac valve surgery. This observational cohort study used The Society of Thoracic Surgeon (STS) Adult Cardiac Surgery Database data to identify and quantify the predictors of intraoperative TEE use among adult patients aged 18 years or more undergoing either isolated CABG or open cardiac valve repair or replacement surgery between January 1, 2011, and December 31, 2019. Generalized linear mixed models were used to measure the relationship between intraoperative TEE and patient characteristics, surgical volume, and geographic location, while accounting for clustering within hospitals (primary analysis) or surgeons (secondary analysis). Of 1,973,655 patients, 1,365,708 underwent isolated CABG and 607,947 underwent cardiac valve surgery. Overall, intraoperative TEE was used in 62% of surgeries. The primary hospital-level generalized linear mixed models analysis demonstrated that the strongest predictor of intraoperative TEE use was the hospital where the surgery occurred—with a median odds ratio for TEE of 10.13 in isolated CABG and 5.30 in cardiac valve surgery. The secondary surgeon-level generalized linear mixed models analysis demonstrated similar findings. Intraoperative TEE use (vs lack of use) during surgery was more strongly associated with hospital and surgeon practice patterns than with any patient-level factor, surgical volume, or geographic location.

Subclassification and matching are often used to adjust for observed covariates in observational studies; however, they are largely restricted to relatively simple study designs with a binary treatment. One important exception is Lu et al.(2001), who considered optimal pair matching with a continuous treatment dose. In this article, we propose two criteria for optimal subclassification/full matching based on subclass homogeneity with a continuous treatment dose, and propose an efficient polynomial-time algorithm that is guaranteed to find an optimal subclassification with respect to one criterion and serves as a 2-approximation algorithm for the other criterion. We discuss how to incorporate treatment dose and use appropriate penalties to control the number of subclasses in the design. Via extensive simulations, we systematically examine the performance of our proposed method, and demonstrate that combining our proposed subclassification scheme with regression adjustment helps reduce model dependence for parametric causal inference with a continuous treatment dose. We illustrate the new design and how to conduct randomization-based statistical inference under the new design using Medicare and Medicaid claims data to study the effect of transesophageal echocardiography (TEE) during CABG surgery on patients' 30-day mortality rate.

Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined optimal DTRs with a time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions only partially identified. We derive a novel Bellman equation under partial identification, use it to define a generic class of estimands (termed IV-optimal DTRs), and study the associated estimation problem. We then extend the IV-optimality framework to tackle the policy improvement problem, delivering IV-improved DTRs that are guaranteed to perform no worse and potentially better than a pre-specified baseline DTR. Importantly, our IV-improvement framework opens up the possibility of strictly improving upon DTRs that are optimal under the no unmeasured confounding assumption (NUCA). We demonstrate via extensive simulations the superior performance of IV-optimal and IV-improved DTRs over the DTRs that are optimal only under the NUCA. In a real data example, we embed retrospective observational registry data into a natural, two-stage experiment with noncompliance using a time-varying IV and estimate useful IV-optimal DTRs that assign mothers to high-level or low-level neonatal intensive care units based on their prognostic variables.

Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme forms of social distancing like isolation and quarantine have costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to test the causal null hypothesis and make inference about the dose-response relationship between reduction in social mobility and COVID-19 related public health outcomes. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent treatment dose in a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.

One central goal of design of observational studies is to embed non-experimental data into an approximate randomized controlled trial using statistical matching. Researchers then make the randomization assumption in their downstream, outcome analysis. For matched pair design, the randomization assumption states that the treatment assignment across all matched pairs are independent, and that the probability of the first subject in each pair receiving treatment and the other control is the same as the first receiving control and the other treatment. In this article, we develop a novel framework for testing the randomization assumption based on solving a clustering problem with side-information using modern statistical learning tools. Our testing framework is nonparametric, finite-sample exact, and distinct from previous proposals in that it can be used to test a relaxed version of the randomization assumption called the biased randomization assumption. One important by-product of our testing framework is a quantity called residual sensitivity value (RSV), which quantifies the level of minimal residual confounding due to observed covariates not being well matched. We advocate taking into account RSV in the downstream primary analysis. The proposed methodology is illustrated by re-examining a famous observational study concerning the effect of right heart catheterization (RHC) in the initial care of critically ill patients.

Nearly 150,000 patients undergo open cardiac valve or aortic surgery each year in the US. Intraoperative transesophageal echocardiography (TEE) is used frequently during cardiac surgery, but there is a lack of evidence associating TEE use to improved clinical outcomes. This matched, retrospective cohort study used national registry data from the Society of Thoracic Surgeon (STS), Adult Cardiac Surgery Database (ACSD) between 2011–2019 to compare clinical outcomes among patients undergoing cardiac valve or aortic surgery with vs without intraoperative TEE. Statistical analyses consisted of multiple matched comparisons (including within-hospital and within-surgeon matches), a negative control outcome analysis, and sensitivity analyses.

In malaria endemic areas, a high proportion of children have detectable parasitemia but show no clinical symptoms. When comatose from a cause other than malaria, this group confounds the cerebral malaria (CM) definition, making accurate diagnosis challenging. One important biomarker of CM is malarial retinopathy, a set of specific features visible in the ocular fundus. In this study, we quantified the contribution of malarial retinopathy in discriminating malaria-caused coma from non-malaria-caused coma. We estimated that 10% of our study cohort of n = 1,192 patients who met the WHO clinical definition of CM in Malawi had non-malarial coma based on a Gaussian mixture model using the parasite protein Plasmodium falciparum histidine-rich protein-2 (PfHRP2). A classification based on platelets, white blood cells and retinopathy significantly improved the discriminative power of a previously established model including only platelets plus white blood cells (AUROC: 0.89 vs. 0.75, p-value < 0.001). We conclude that malarial retinopathy is highly predictive of malaria-caused vs. non-malaria-caused coma and recommend an ocular funduscopic examination to determine malarial retinopathy status be included in the assessment of parasitemic comatose African children.

The systolic, diastolic, and mean CBFVs and Windkessel notch explain more than 98% of total TCD data variation. Statistical learning algorithm identified 7 phenotypic clusters for the MCA TCD data.Phenotypic clusters are not associated with demographics, clinical features or laboratory measurements. TCD measurements are associated with survival hazards rates after adjusting for age and gender.