The impact of aggressive and conservative propensity for initiation of neuromuscular blockade in mechanically ventilated patients with hypoxemic respiratory failure.

INTRODUCTION: Neuromuscular blockade (NMB) in ventilated patients may cause benefit or harm. We applied "incremental interventions" to determine the impact of altering NMB initiation aggressiveness. METHODS: Retrospective cohort study of ventilated patients with PaO2/FiO2 ratio < 150 mmHg and PEEP≥ 8cmH2O from the Medical Information Mart of Intensive Care IV database (MIMIC-IV version 1.0) estimating the effect of incremental interventions on in-hospital mortality and ventilator-free days, modifying hourly propensity for NMB initiation to be aggressive or conservative relative to usual care, adjusting for confounding with inverse probability weighting. RESULTS: 5221 patients were included (13.3% initiated on NMB). Incremental interventions estimated a strong effect on NMB usage: 5-fold higher hourly odds of initiation increased usage to 36.5% (CI = [34.3%,38.7%]) and 5-fold lower odds decreased usage to 3.8% (CI = [3.3%,4.3%]). Aggressive and conservative strategies demonstrated a U-shaped mortality relationship. 5-fold higher or lower propensity increased in-hospital mortality by 2.6% (0.95 CI = [1.5%,3.7%]) or 1.3% (0.95 CI = [0.1%,2.5%]) respectively. In secondary analysis of a healthier patient cohort, results were similar, however conservative strategies also improved ventilator-free days. INTERPRETATION: Aggressive or conservative initiation of NMB may worsen mortality. In healthier populations, marginally conservative NMB initiation strategies may lead to increased ventilator free days with minimal impact on mortality.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California.

The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often used to estimate the population size yet may produce implausible and unreliable estimates due to model misspecification and small cell sizes. A novel targeted minimum loss-based estimation (TMLE) model developed for capture-recapture makes several notable improvements to conventional modeling: "targeting" the parameter of interest, flexibly fitting the data to alternative functional forms, and limiting bias from small cell sizes. Using simulations and empirical data from the San Francisco, California, Department of Public Health's human immunodeficiency virus (HIV) surveillance registry, we evaluated the performance of the TMLE model and compared results with those of other common models. Based on 2,584 people observed on 3 lists reportable to the surveillance registry, the TMLE model estimated the number of San Francisco residents living with HIV as of December 31, 2019, to be 13,523 (95% confidence interval: 12,222, 14,824). This estimate, compared with a "ground truth" of 12,507, was the most accurate and precise of all models examined. The TMLE model is a significant advancement in capture-recapture studies, leveraging modern statistical methods to improve estimation of the sizes of hidden populations.

Like-minded sources on Facebook are prevalent but not polarizing.

Many critics raise concerns about the prevalence of 'echo chambers' on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from 'like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

How do social media feed algorithms affect attitudes and behavior in an election campaign?

We investigated the effects of Facebook's and Instagram's feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users' on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

Reshares on social media amplify political news but do not detectably affect beliefs or opinions.

We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.

Association Between Stimulant Treatment and Substance Use Through Adolescence Into Early Adulthood.

Importance: Possible associations between stimulant treatment of attention-deficit/hyperactivity disorder (ADHD) and subsequent substance use remain debated and clinically relevant. Objective: To assess the association of stimulant treatment of ADHD with subsequent substance use using the Multimodal Treatment Study of ADHD (MTA), which provides a unique opportunity to test this association while addressing methodologic complexities (principally, multiple dynamic confounding variables). Design, Setting, and Participants: MTA was a multisite study initiated at 6 sites in the US and 1 in Canada as a 14-month randomized clinical trial of medication and behavior therapy for ADHD but transitioned to a longitudinal observational study. Participants were recruited between 1994 and 1996. Multi-informant assessments included comprehensively assessed demographic, clinical (including substance use), and treatment (including stimulant treatment) variables. Children aged 7 to 9 years with rigorously diagnosed DSM-IV combined-type ADHD were repeatedly assessed until a mean age of 25 years. Analysis took place between April 2018 and February 2023. Exposure: Stimulant treatment of ADHD was measured prospectively from baseline for 16 years (10 assessments) initially using parent report followed by young adult report. Main Outcomes and Measures: Frequency of heavy drinking, marijuana use, daily cigarette smoking, and other substance use were confidentially self-reported with a standardized substance use questionnaire. Results: A total of 579 children (mean [SD] age at baseline, 8.5 [0.8] years; 465 [80%] male) were analyzed. Generalized multilevel linear models showed no evidence that current (B [SE] range, -0.62 [0.55] to 0.34 [0.47]) or prior stimulant treatment (B [SE] range, -0.06 [0.26] to 0.70 [0.37]) or their interaction (B [SE] range, -0.49 [0.70] to 0.86 [0.68]) were associated with substance use after adjusting for developmental trends in substance use and age. Marginal structural models adjusting for dynamic confounding by demographic, clinical, and familial factors revealed no evidence that more years of stimulant treatment (B [SE] range, -0.003 [0.01] to 0.04 [0.02]) or continuous, uninterrupted stimulant treatment (B [SE] range, -0.25 [0.33] to -0.03 [0.10]) were associated with adulthood substance use. Findings were the same for substance use disorder as outcome. Conclusions and Relevance: This study found no evidence that stimulant treatment was associated with increased or decreased risk for later frequent use of alcohol, marijuana, cigarette smoking, or other substances used for adolescents and young adults with childhood ADHD. These findings do not appear to result from other factors that might drive treatment over time and findings held even after considering opposing age-related trends in stimulant treatment and substance use.

Is the Association Between Fruits and Vegetables and Preeclampsia Due to Higher Dietary Vitamin C and Carotenoid Intakes?

BACKGROUND: Diets dense in fruits and vegetables are associated with a reduced risk of preeclampsia, but pathways underlying this relationship are unclear. Dietary antioxidants may contribute to the protective effect. OBJECTIVE: We determined the extent to which the effect of dietary fruit and vegetable density on preeclampsia is because of high intakes of dietary vitamin C and carotenoids. METHODS: We used data from 7572 participants in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (8 United States medical centers, 2010‒2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We estimated the indirect effect of ≥2.5 cups/1000 kcal of fruits and vegetables through vitamin C and carotenoid on the risk of preeclampsia. We estimated these effects using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders, including other dietary components, health behaviors, and psychological, neighborhood, and sociodemographic factors. RESULTS: Participants who consumed ≥2.5 cups of fruits and vegetables per 1000 kcal were less likely than those who consumed <2.5 cups/1000 kcal to develop preeclampsia (6.4% compared with 8.6%). After confounder adjustment, we observed that higher fruit and vegetable density was associated with 2 fewer cases of preeclampsia (risk difference: -2.0; 95% CI: -3.9, -0.1)/100 pregnancies compared with lower density diets. High dietary vitamin C and carotenoid intake was not associated with preeclampsia. The protective effect of high fruit and vegetable density on the risk of preeclampsia and late-onset preeclampsia was not mediated through dietary vitamin C and carotenoids. CONCLUSIONS: Evaluating other nutrients and bioactives in fruits and vegetables and their synergy is worthwhile, along with characterizing the effect of individual fruits or vegetables on preeclampsia risk.

Seneca Valley virus replicons are packaged in trans and have the capacity to overcome the limitations of viral transgene expression.

Oncolytic viruses (OVs) promote the anti-tumor immune response as their replication, and the subsequent lysis of tumor cells, triggers the activation of immune-sensing pathways. Arming OVs by expressing transgenes with the potential to promote immune cell recruitment and activation is an attractive strategy to enhance OVs' therapeutic benefit. For picornaviruses, a family of OVs with clinical experience, the expression of a transgene is limited by multiple factors: genome physical packaging limits, high rates of recombination, and viral-mediated inhibition of transgene secretion. Here, we evaluated strategies for arming Seneca Valley virus (SVV) with relevant immunomodulatory transgenes. Specificially in the contex of arming SVV, we evaluated transgene maximum size and stabiltity, transgene secretion, and the impact of transgene inclusion on viral fitness. We find that SVV is not capable of expressing secreted payloads and has a transgene packaging capacity of ∼10% of viral genome size. To enable transgene expression, we developed SVV replicons with greater transgene size capacity and secretion capabilities. SVV replicons can be packaged in trans by virus in co-infected cells to express immunomodulatory transgenes in surrounding cells, thus providing a means to enhance the potential of this therapeutic to augment the anti-tumor immune response.

Doubly robust nonparametric instrumental variable estimators for survival outcomes.

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this article, we propose nonparametric estimators for the local average treatment effect on survival probabilities under both covariate-dependent and outcome-dependent censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and double robustness of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

Estimation of the Time-Varying Incremental Effect of Low-dose Aspirin on Incidence of Pregnancy.

BACKGROUND: In many research settings, the intervention implied by the average causal effect of a time-varying exposure is impractical or unrealistic, and we might instead prefer a more realistic target estimand. Instead of requiring all individuals to be always exposed versus unexposed, incremental effects quantify the impact of merely shifting each individual's probability of being exposed. METHODS: We demonstrate the estimation of incremental effects in the time-varying setting, using data from the Effects of Aspirin in Gestation and Reproduction trial, which assessed the effect of preconception low-dose aspirin on pregnancy outcomes. Compliance to aspirin or placebo was summarized weekly and was affected by time-varying confounders such as bleeding or nausea. We sought to estimate what the incidence of pregnancy by 26 weeks postrandomization would have been if we shifted each participant's probability of taking aspirin or placebo each week by odds ratios (OR) between 0.30 and 3.00. RESULTS: Under no intervention (OR = 1), the incidence of pregnancy was 77% (95% CI: 74%, 80%). Decreasing women's probability of complying with aspirin had little estimated effect on pregnancy incidence. When we increased women's probability of taking aspirin, estimated incidence of pregnancy increased, from 83% (95% confidence interval [CI] = 79%, 87%) for OR = 2 to 89% (95% CI = 84%, 93%) for OR=3. We observed similar results when we shifted women's probability of complying with a placebo. CONCLUSIONS: These results estimated that realistic interventions to increase women's probability of taking aspirin would have yielded little to no impact on the incidence of pregnancy, relative to similar interventions on placebo.

Marginal structural models for estimating the longitudinal effects of community violence exposure on youths' internalizing and externalizing symptoms.

OBJECTIVE: Longitudinal observational data pose a challenge for causal inference when the exposure of interest varies over time alongside time-dependent confounders, which often occurs in trauma research. We describe marginal structural models (MSMs) using inverse probability weighting as a useful solution under several assumptions that are well-suited to estimating causal effects in trauma research. METHOD: We illustrate the application of MSMs by estimating the joint effects of community violence exposure across time on youths' internalizing and externalizing symptoms. Our sample included 4,327 youth (50% female, 50% male; 1.4% Asian American or Pacific Islander, 34.7% Black, 46.9% Hispanic, .8% Native American, 14.3%, White, 1.5%, Other race/ethnicity; Mage at baseline = 8.62, range = 3-15) from the Project on Human Development in Chicago Neighborhoods. RESULTS: Wave 3 internalizing symptoms increased linearly with increases in Wave 2 and Wave 3 community violence exposure, whereas effects on externalizing symptoms were quadratic for Wave 2 community violence exposure and linear for Wave 3. These results fail to provide support for the desensitization model of community violence exposure. CONCLUSION: MSMs are a useful tool for researchers who rely on longitudinal observational data to estimate causal effects of time-varying exposures, as is often the case in the study of psychological trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Development of intravenously administered synthetic RNA virus immunotherapy for the treatment of cancer.

The therapeutic effectiveness of oncolytic viruses (OVs) delivered intravenously is limited by the development of neutralizing antibody responses against the virus. To circumvent this limitation and to enable repeated systemic administration of OVs, here we develop Synthetic RNA viruses consisting of a viral RNA genome (vRNA) formulated within lipid nanoparticles. For two Synthetic RNA virus drug candidates, Seneca Valley virus (SVV) and Coxsackievirus A21, we demonstrate vRNA delivery and replication, virus assembly, spread and lysis of tumor cells leading to potent anti-tumor efficacy, even in the presence of OV neutralizing antibodies in the bloodstream. Synthetic-SVV replication in tumors promotes immune cell infiltration, remodeling of the tumor microenvironment, and enhances the activity of anti-PD-1 checkpoint inhibitor. In mouse and non-human primates, Synthetic-SVV is well tolerated reaching exposure well above the requirement for anti-tumor activity. Altogether, the Synthetic RNA virus platform provides an approach that enables repeat intravenous administration of viral immunotherapy.

Estimation of the Average Causal Effect in Longitudinal Data With Time-Varying Exposures: The Challenge of Nonpositivity and the Impact of Model Flexibility.

There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: -9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: -1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.

Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial.

Importance: In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as is common with standard methods (eg, logistic regression). Objectives: To demonstrate the use of ensemble machine learning with augmented inverse probability weighting (AIPW) for per-protocol effect estimation in RCTs and to evaluate the per-protocol effect size of aspirin on pregnancy. Design, Setting, and Participants: This secondary analysis used data from 1227 women in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial, a multicenter, block-randomized, double-blind, placebo-controlled clinical trial of the effect of daily low-dose aspirin on pregnancy outcomes in women at high risk of pregnancy loss. Participants were recruited at 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Women were followed up for 6 menstrual cycles for attempted pregnancy and 36 weeks of gestation if pregnancy occurred. Follow-up was completed on August 17, 2012. Data analyses were performed on July 9, 2021. Exposures: Daily low-dose (81 mg) aspirin taken at least 5 of 7 days per week for at least 80% of follow-up time relative to placebo. Main Outcomes and Measures: Pregnancy detected using human chorionic gonadotropin (hCG) levels. Results: Among the 1227 women included in the analysis (mean SD age, 28.74 [4.80] years), 1161 (94.6%) were non-Hispanic White and 858 (69.9%) adhered to the protocol. Five machine learning models were combined into 1 meta-algorithm, which was used to construct an AIPW estimator for the per-protocol effect. Compared with adhering to placebo, adherence to the daily low-dose aspirin protocol for at least 5 of 7 days per week was associated with an increase in the probability of hCG-detected pregnancy of 8.0 (95% CI, 2.5-13.6) more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3 (95% CI, -1.1 to 9.6) more hCG-detected pregnancies per 100 women in the sample. Conclusions and Relevance: These findings suggest that a low-dose aspirin protocol is associated with increased hCG-detected pregnancy in women who adhere to treatment for at least 5 days per week. With the presence of nonadherence, per-protocol treatment effect estimates differ from intention-to-treat estimates in the EAGeR trial. The results of this secondary analysis of clinical trial data suggest that machine learning could be used to estimate per-protocol effects by adjusting for confounders related to nonadherence in a more flexible way than traditional regressions. Trial Registration: ClinicalTrials.gov Identifier: NCT00467363.

Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia.

The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and vegetable density (cup-equivalents/1,000 kcal) and preeclampsia using data from a pregnancy cohort study conducted at 8 US medical centers (n = 9,412; 2010-2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We quantified the effects of diets with a high density of fruits (≥1.2 cups/1,000 kcal/day vs. <1.2 cups/1,000 kcal/day) and vegetables (≥1.3 cups/1,000 kcal/day vs. <1.3 cups/1,000 kcal/day) on preeclampsia risk, conditional on BMI, using a doubly robust estimator implemented in 2 stages. We found that the protective association of higher fruit density declined approximately linearly from a BMI of 20 to a BMI of 32, by 0.25 cases per 100 women per each BMI unit, and then flattened. The protective association of higher vegetable density strengthened in a linear fashion, by 0.3 cases per 100 women for every unit increase in BMI, up to a BMI of 30, where it plateaued. Dietary patterns with a high periconceptional density of fruits and vegetables appear more protective against preeclampsia for women with higher BMI than for leaner women.

Doubly robust adaptive LASSO for effect modifier discovery.

Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.

Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification.

Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification.

Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis.

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.