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Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Neutralizing antibody titer has been a surrogate endpoint for guiding COVID-19 vaccine approval and use, although the pandemic’s evolution and the introduction of variant-adapted vaccine boosters raise questions as to this surrogate’s contemporary performance. For 985 recipients of an mRNA second bivalent or monovalent booster containing various Spike inserts [Prototype (Ancestral), Beta, Delta, and/or Omicron BA.1 or BA.4/5] in the COVAIL trial (NCT05289037), titers against 5 strains were assessed as correlates of risk of symptomatic COVID-19 (“COVID-19”) and as correlates of relative (Pfizer-BioNTech Omicron vs. Prototype) booster protection against COVID-19 over 6 months of follow-up during the BA.2-BA.5 Omicron-dominant period. Consistently across the Moderna and Pfizer-BioNTech vaccine platforms and across all variant Spike inserts assessed, both peak and exposure-proximal (“predicted-at-exposure”) titers correlated with lower Omicron COVID-19 risk in individuals previously infected with SARS-CoV-2, albeit significantly less so in naïve individuals [e.g., exposure-proximal hazard ratio per 10-fold increase in BA.1 titer 0.74 (95% CI 0.59, 0.94) for naïve vs. 0.41 (95% CI 0.23, 0.64) for non-naïve; interaction p = 0.013]. Neutralizing antibody titer was a strong inverse correlate of Omicron COVID-19 in non-naïve individuals and a weaker correlate in naïve individuals, posing questions about how prior infection alters the neutralization correlate. Here the authors analyze data from COVAIL trial participants receiving an mRNA second COVID-19 vaccine boost and show that, while neutralizing antibody titer is correlated with Omicron COVID-19 risk, the correlation is weak in naïve individuals compared to previously infected ones. _

Proteins expressed on the surface of -infected erythrocytes (IEs) are targets of immunity. Previous studies described that IEs from young children are more readily recognized than IEs from older children. Here, we aimed to identify targets of naturally acquired antibodies that may play a role in protection from malaria. We applied immunoprecipitation followed by mass spectrometry (IP-MS) using plasma samples from susceptible and semi-immune children. We then investigated whether 1) antibody levels to membrane-associated proteins identified by IP-MS, or 2) PfEMP1s expressed in IEs of young children predict reduction in malaria disease. Significant reduction in risk of high parasite density infection was predicted by high antibody levels to DBLγ11 [OR (95%CI): 0.74 (0.63-0.86)] and DBLζ5 [0.80 (0.69-0.93)], as well as two helical interspersed subtelomeric proteins: PF3D7_0201600 [0.81 (0.70-0.94)] and PF3D7_0532300 [0.79 (0.68-0.92)]. Opsonic phagocytosis of DBLγ11 and DBLζ5 coated beads was significantly higher in plasma samples with antibody levels at the top tertile compared to those from lower tertiles (p<0.0001). The Relative phagocytosis index positively correlated with antibody levels, r = 0.61 (p<0.001) and r = 0.49 (p<0.001) for DBLζ5 and DBLγ11, respectively. The study showed that high antibody levels against four proteins significantly reduced the odds of high parasite burden in future infections. Our functional study suggests that opsonic phagocytosis may mediate reduction in parasite density by antibodies to DBLγ11 and DBLζ5.

Introduction:Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study.Methods:The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis.Results:In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of –1.53%-points (95% CI –2.75%-points to –0.30%-points).Conclusions:The Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.

Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the , we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study. The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis. In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of -1.53%-points (95% CI -2.75%-points to -0.30%-points). The helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.

IntroductionRisk factors for interpersonal violence-related injury (IPVRI) in low-income and middle-income countries (LMICs) remain poorly defined. We describe associations between IPVRI and select social determinants of health (SDH) in Cameroon.MethodsWe conducted a cross-sectional analysis of prospective trauma registry data collected from injured patients >15 years old between October 2017 and January 2020 at four Cameroonian hospitals. Our primary outcome was IPVRI, compared with unintentional injury. Explanatory SDH variables included education level, employment status, household socioeconomic status (SES) and alcohol use. The EconomicClusters model grouped patients into household SES clusters: rural, urban poor, urban middle-class (MC) homeowners, urban MC tenants and urban wealthy. Results were stratified by sex. Categorical variables were compared via Pearson’s χ2 statistic. Associations with IPVRI were estimated using adjusted odds ratios (aOR) with 95% confidence intervals (95%CI).ResultsAmong 7605 patients, 5488 (72.2%) were men. Unemployment was associated with increased odds of IPVRI for men (aOR 2.44 (95% CI 1.95 to 3.06), p<0.001) and women (aOR 2.53 (95% CI 1.35 to 4.72), p=0.004), as was alcohol use (men: aOR 2.33 (95% CI 1.91 to 2.83), p<0.001; women: aOR 3.71 (95% CI 2.41 to 5.72), p<0.001). Male patients from rural (aOR 1.45 (95% CI 1.04 to 2.03), p=0.028) or urban poor (aOR 2.08 (95% CI 1.27 to 3.41), p=0.004) compared with urban wealthy households had increased odds of IPVRI, as did female patients with primary-level/no formal (aOR 1.78 (95% CI 1.10 to 2.87), p=0.019) or secondary-level (aOR 1.54 (95% CI 1.03 to 2.32), p=0.037) compared with tertiary-level education.ConclusionLower educational attainment, unemployment, lower household SES and alcohol use are risk factors for IPVRI in Cameroon. Future research should explore LMIC-appropriate interventions to address SDH risk factors for IPVRI.

This dissertation encompasses the development and application of the experiment-selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) for analyzing hybrid randomized-external data studies. The goal of these hybrid designs is to augment a small randomized controlled trial (RCT) with external data – in the form of the control arm(s) of previous trials or real-world healthcare data (RWD) – in order to increase power. Of course, inclusion of RWD may also increase the causal gap, defined as the difference between the causal effect of interest and the statistical parameter that we will estimate from the data. The primary statistical challenges are 1) excluding external data that would introduce bias of a magnitude large enough to worsen coverage for the causal effect while still including unbiased external data frequently enough to improve power and 2) constructing confidence intervals that appropriately reflect that the causal gap may not be zero when external data are integrated. In Chapter 1, we describe the development of the ES-CVTMLE methodology, focusing on the case where only external controls are available. We consider two methods of estimating the causal gap: 1) a function of the difference in conditional mean outcome under control between the RCT and combined experiments and 2) the estimated average treatment effect on a negative control outcome. We then define criteria for selecting the experiment (RCT alone or RCT combined with external data) that optimizes the estimated bias-variance tradeoff. To separate the data used for experiment selection from the data used for effect estimation, we develop an experiment-selector cross-validated targeted maximum likelihood estimator. We define the asymptotic distribution of the ES-CVTMLE under varying magnitudes of bias and construct confidence intervals by Monte Carlo simulation. We demonstrate the performance of the ES-CVTMLE compared to three other estimators for hybrid randomized-external data designs using simulations and a re-analysis of the LEADER trial of the effect of liraglutide versus placebo on cardiovascular outcomes. In Chapter 2, we describe the development of the EScvtmle R software package to implement the method described in Chapter 1. The software package also extends this methodology to allow for integration of external data participants with both the active treatment and control arms of the trial. We include vignettes demonstrating use of the EScvtmle package with the publicly available WASH Benefits Bangladesh cluster RCT dataset. The real data examples in Chapters 1 and 2 rely on following the Roadmap for Causal and Statistical Inference, a structured process that guides the design, analysis, and interpretation of studies anywhere on the spectrum from a traditional RCT to a fully observational study. In Chapter 3, we describe this Causal Roadmap to an audience of clinical and translational researchers. We also extend the Roadmap framework to consider how outcome-blind simulations may be used for quantitative comparison of the characteristics of different potential study designs. Chapter 4 represents the culmination of the previous work; we use a case study of semaglutide and cardiovascular outcomes to demonstrate application of this extended version of the Causal Roadmap to compare study designs involving traditional RCTs with a hybrid randomized-external data design. We demonstrate how following the Causal Roadmap can help to define an external control arm in a way that improves the plausibility of causal identification assumptions. We then use simulations to demonstrate the tradeoffs between each of these potential designs. Finally, we present a real data analysis using the ES-CVTMLE to estimate the effect of oral semaglutide versus standard-of-care on major adverse cardiovascular events based on the PIONEER 6 RCT and considering augmentation with RWD from Optum’s de-identified Clinformatics Data Mart Database (CDM) (2007-2022).

In self-amplifying RNA (saRNA), substitution of cytidine with 5-hydroxymethylcytidine (hm5C) reduces innate immune responses and prolongs protein expression. When formulated as a vaccine and administered intramuscularly, lipid nanoparticles (LNPs) loaded with modified saRNA (saRNA-LNPs) afford robust and long-term protein expression. Here we report the protein expression and cell type tropism of modified saRNA-LNPs, encoding fluorescent proteins, when injected in the mammalian brain. saRNA encapsulated in an LNP formulation comprising ALC-0315 (present in Comirnaty ) efficiently mediates robust and long-lasting protein expression in brain cells beyond five weeks, with detectable expression in some neurons at three months. hm5C saRNA substantially outperforms N1mΨ mRNA. Intriguingly, in addition to transfecting astrocytes and neurons at the injection site, saRNA-LNPs labels neurons retrogradely. Thus, saRNA-LNPs are an exciting nonviral gene transduction method that effectively transduces brain cells with excellent potency and mediates prolonged gene expression.

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to ensure that analyses meet their intended goals. The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative -- a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.