Causal survival analysis under competing risks using longitudinal modified treatment policies

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as $\sqrt{n}$-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event

Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures

This tutorial discusses a recently developed methodology for causal inference based on longitudinal modified treatment policies (LMTPs). LMTPs generalize many commonly used parameters for causal inference including average treatment effects, and facilitate the mathematical formalization, identification, and estimation of many novel parameters. LMTPs apply to a wide variety of exposures, including binary, multivariate, and continuous, as well as interventions that result in violations of the positivity assumption. LMTPs can accommodate time-varying treatments and confounders, competing risks, loss-to-follow-up, as well as survival, binary, or continuous outcomes. This tutorial aims to illustrate several practical uses of the LMTP framework, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions which can be answered within the proposed framework. We go into more depth with one of these examples -- specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data

Studies often report estimates of the average treatment effect. While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule. Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.Comment: Accepted for publication in American Journal of Epidemiolog

Age at First Concussion Influences Number of Subsequent Concussions

Background: Individuals that sustain their first concussion during childhood may be at greater risk for sustaining multiple concussions throughout their lifetime, due to a longer window of vulnerability. Purpose: To estimate the association between age at first concussion with number of subsequent concussions. Methods: A total of 23,582 collegiate athletes from 26 universities and military cadets from three military academies completed a concussion history questionnaire (65% males, age: 19.9±1.4years). Participants self-reported concussions and age at time of each injury. Participants with a history of concussion (n=3,647, 15.5%) were categorized as having sustained their first concussion during childhood (<10 years old - yo) or adolescence (≥10yo & ≤18yo). Poisson regression was used to model age group (childhood, adolescence) predicting number of subsequent concussions (0, 1, 2+). A second Poisson regression was developed to determine whether age at first concussion predicted number of subsequent concussions. Results: Participants self-reporting their first concussion during childhood had an increased risk of sustaining subsequent concussions (RR=2.19, 95% CI: 1.82, 2.64) compared to participants self-reporting their first concussion during adolescence. For every one-year increase in age at first concussion, we observed a 16% reduction in the risk of subsequent concussion (RR=0.84, 95% CI:0.82,0.86). Conclusion(s): Individuals self-reporting a concussion at a young age sustained a higher number of concussions prior to the age of 18. Concussion prevention, recognition, and reporting strategies are of particular need at the youth level

Age at First Concussion Influences Number of Subsequent Concussions

Background: Individuals that sustain their first concussion during childhood may be at greater risk for sustaining multiple concussions throughout their lifetime, due to a longer window of vulnerability. Purpose: To estimate the association between age at first concussion with number of subsequent concussions. Methods: A total of 23,582 collegiate athletes from 26 universities and military cadets from three military academies completed a concussion history questionnaire (65% males, age: 19.9±1.4years). Participants self-reported concussions and age at time of each injury. Participants with a history of concussion (n=3,647, 15.5%) were categorized as having sustained their first concussion during childhood (<10 years old - yo) or adolescence (≥10yo & ≤18yo). Poisson regression was used to model age group (childhood, adolescence) predicting number of subsequent concussions (0, 1, 2+). A second Poisson regression was developed to determine whether age at first concussion predicted number of subsequent concussions. Results: Participants self-reporting their first concussion during childhood had an increased risk of sustaining subsequent concussions (RR=2.19, 95% CI: 1.82, 2.64) compared to participants self-reporting their first concussion during adolescence. For every one-year increase in age at first concussion, we observed a 16% reduction in the risk of subsequent concussion (RR=0.84, 95% CI:0.82,0.86). Conclusion(s): Individuals self-reporting a concussion at a young age sustained a higher number of concussions prior to the age of 18. Concussion prevention, recognition, and reporting strategies are of particular need at the youth level

Subcortical Brain Volumes and Neurocognitive Function in Children With Perinatal HIV Exposure: A Population-Based Cohort Study in South Africa

BACKGROUND: Children who are HIV-exposed and uninfected (HEU) are at risk for early neurodevelopmental impairment. Smaller basal ganglia nuclei have been reported in neonates who are HEU compared to HIV-unexposed (HU); however, neuroimaging studies outside infancy are scarce. We examined subcortical brain structures and associations with neurocognition in children who are HEU. METHODS: This neuroimaging study was nested within the Drakenstein Child Health Study birth cohort in South Africa. We compared (T1-weighted) magnetic resonance imaging–derived subcortical brain volumes between children who were HEU (n = 70) and HU (n = 92) at age 2–3 years using linear regression. Brain volumes were correlated with neurodevelopmental outcomes measured with the Bayley Scales of Infant and Toddler Development III. RESULTS: Compared to HU children, on average children who were HEU had 3% lower subcortical grey matter volumes. Analyses of individual structures found smaller volume of the putamen nucleus in the basal ganglia (−5% difference, P = .016) and the hippocampus (−3% difference, P = .044), which held on adjustment for potential confounders (P < .05). Maternal viremia and lower CD4 count in pregnancy were associated with smaller child putamen volumes. Children who were HEU had lower language scores than HU; putamen and hippocampus volumes were positively correlated with language outcomes. CONCLUSIONS: Overall, children who are HEU had a pattern of smaller subcortical volumes in the basal ganglia and hippocampal regions compared to HU children, which correlated with language function. Findings suggest that optimizing maternal perinatal HIV care is important for child brain development. Further studies are needed to investigate underlying mechanisms and long-term outcomes

Restoring Cystic Fibrosis Transmembrane Conductance Regulator Function Reduces Airway Bacteria and Inflammation in People with Cystic Fibrosis and Chronic Lung Infections

Rationale: Previous work indicates that ivacaftor improves cystic fibrosis transmembrane conductance regulator (CFTR) activity and lung function in people with cystic fibrosis and G551D-CFTR mutations but does not reduce density of bacteria or markers of inflammation in the airway. These findings raise the possibility that infection and inflammation may progress independently of CFTR activity once cystic fibrosis lung disease is established. Objectives: To better understand the relationship between CFTR activity, airway microbiology and inflammation, and lung function in subjects with cystic fibrosis and chronic airway infections. Methods: We studied 12 subjects with G551D-CFTR mutations and chronic airway infections before and after ivacaftor. We measured lung function, sputum bacterial content, and inflammation, and obtained chest computed tomography scans. Measurements and Main Results: Ivacaftor produced rapid decreases in sputum Pseudomonas aeruginosa density that began within 48 hours and continued in the first year of treatment. However, no subject eradicated their infecting P. aeruginosa strain, and after the first year P. aeruginosa densities rebounded. Sputum total bacterial concentrations also decreased, but less than P. aeruginosa. Sputum inflammatory measures decreased significantly in the first week of treatment and continued to decline over 2 years. Computed tomography scans obtained before and 1 year after ivacaftor treatment revealed that ivacaftor decreased airway mucous plugging. Conclusions: Ivacaftor caused marked reductions in sputum P. aeruginosa density and airway inflammation and produced modest improvements in radiographic lung disease in subjects with G551D-CFTR mutations. However, P. aeruginosa airway infection persisted. Thus, measures that control infection may be required to realize the full benefits of CFTR-targeting treatments

A functional definition to distinguish ponds from lakes and wetlands

Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems.Fil: Richardson, David C.. State University of New York at New Paltz; Estados UnidosFil: Holgerson, Meredith A.. Cornell University; Estados UnidosFil: Farragher, Matthew J.. University of Maine; Estados UnidosFil: Hoffman, Kathryn K.. No especifíca;Fil: King, Katelyn B. S.. Michigan State University; Estados UnidosFil: Alfonso, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Andersen, Mikkel R.. No especifíca;Fil: Cheruveil, Kendra Spence. Michigan State University; Estados UnidosFil: Coleman, Kristen A.. University of York; Reino UnidoFil: Farruggia, Mary Jade. University of California at Davis; Estados UnidosFil: Fernandez, Rocio Luz. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hondula, Kelly L.. No especifíca;Fil: López Moreira Mazacotte, Gregorio A.. Leibniz - Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Paul, Katherine. No especifíca;Fil: Peierls, Benjamin L.. No especifíca;Fil: Rabaey, Joseph S.. University of Minnesota; Estados UnidosFil: Sadro, Steven. University of California at Davis; Estados UnidosFil: Sánchez, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Smyth, Robyn L.. No especifíca;Fil: Sweetman, Jon N.. State University of Pennsylvania; Estados Unido