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SummaryBackgroundSince the 1918 influenza pandemic, non-randomised studies and small clinical trials have suggested that convalescent plasma or anti-influenza hyperimmune intravenous immunoglobulin (hIVIG) might have clinical benefit for patients with influenza infection, but definitive data do not exist. We aimed to evaluate the safety and efficacy of hIVIG in a randomised controlled trial.MethodsThis randomised, double-blind, placebo-controlled trial was planned for 45 hospitals in Argentina, Australia, Denmark, Greece, Mexico, Spain, Thailand, UK, and the USA over five influenza seasons from 2013–14 to 2017–18. Adults (≥18 years of age) were admitted for hospital treatment with laboratory-confirmed influenza A or B infection and were randomly assigned (1:1) to receive standard care plus either a single 500-mL infusion of high-titre hIVIG (0·25 g/kg bodyweight, 24·75 g maximum; hIVIG group) or saline placebo (placebo group). Eligible patients had a National Early Warning score of 2 points or greater at the time of screening and their symptoms began no more than 7 days before randomisation. Pregnant and breastfeeding women were excluded, as well as any patients for whom the treatment would present a health risk. Separate randomisation schedules were generated for each participating clinical site using permuted block randomisation. Treatment assignments were obtained using a web-based application by the site pharmacist who then masked the solution for infusion. Patients and investigators were masked to study treatment. The primary endpoint was a six-category ordinal outcome of clinical status at day 7, ranging in severity from death to resumption of normal activities after discharge. The choice of day 7 was based on haemagglutination inhibition titres from a pilot study. It was analysed with a proportional odds model, using all six categories to estimate a common odds ratio (OR). An OR greater than 1 indicated that, for a given category, patients in the hIVIG group were more likely to be in a better category than those in the placebo group. Prespecified primary analyses for safety and efficacy were based on patients who received an infusion and for whom eligibility could be confirmed. This trial is registered with ClinicalTrials.gov, NCT02287467.Findings313 patients were enrolled in 34 sites between Dec 11, 2014, and May 28, 2018. We also used data from 16 patients enrolled at seven of the 34 sites during the pilot study between Jan 15, 2014, and April 10, 2014. 168 patients were randomly assigned to the hIVIG group and 161 to the placebo group. 21 patients were excluded (12 from the hIVIG group and 9 from the placebo group) because they did not receive an infusion or their eligibility could not be confirmed. Thus, 308 were included in the primary analysis. hIVIG treatment produced a robust rise in haemagglutination inhibition titres against influenza A and smaller rises in influenza B titres. Based on the proportional odds model, the OR on day 7 was 1·25 (95% CI 0·79–1·97; p=0·33). In subgroup analyses for the pr...

SES significantly mediates racial/ethnic childhood cancer survival disparities for several cancers. However, the proportion of the total race/ethnicity-survival association explained by SES varies between black-white and Hispanic-white comparisons for some cancers, and this suggests that mediation by other factors differs across groups.

Background We aimed to assess the efficacy and safety of two neutralising monoclonal antibody therapies (sotrovimab [Vir Biotechnology and GlaxoSmithKline] and BRII-196 plus BRII-198 [Brii Biosciences]) for adults admitted to hospital for COVID-19 (hereafter referred to as hospitalised) with COVID-19. Methods In this multinational, double-blind, randomised, placebo-controlled, clinical trial (Therapeutics for Inpatients with COVID-19 [TICO]), adults (aged ≥18 years) hospitalised with COVID-19 at 43 hospitals in the USA, Denmark, Switzerland, and Poland were recruited. Patients were eligible if they had laboratory-confirmed SARS-CoV-2 infection and COVID-19 symptoms for up to 12 days. Using a web-based application, participants were randomly assigned (2:1:2:1), stratified by trial site pharmacy, to sotrovimab 500 mg, matching placebo for sotrovimab, BRII-196 1000 mg plus BRII-198 1000 mg, or matching placebo for BRII-196 plus BRII-198, in addition to standard of care. Each study product was administered as a single dose given intravenously over 60 min. The concurrent placebo groups were pooled for analyses. The primary outcome was time to sustained clinical recovery, defined as discharge from the hospital to home and remaining at home for 14 consecutive days, up to day 90 after randomisation. Interim futility analyses were based on two seven-category ordinal outcome scales on day 5 that measured pulmonary status and extrapulmonary complications of COVID-19. The safety outcome was a composite of death, serious adverse events, incident organ failure, and serious coinfection up to day 90 after randomisation. Efficacy and safety outcomes were assessed in the modified intention-to-treat population, defined as all patients randomly assigned to treatment who started the study infusion. This study is registered with ClinicalTrials.gov , NCT04501978 . Findings Between Dec 16, 2020, and March 1, 2021, 546 patients were enrolled and randomly assigned to sotrovimab (n=184), BRII-196 plus BRII-198 (n=183), or placebo (n=179), of whom 536 received part or all of their assigned study drug (sotrovimab n=182, BRII-196 plus BRII-198 n=176, or placebo n=178; median age of 60 years [IQR 50–72], 228 [43%] patients were female and 308 [57%] were male). At this point, enrolment was halted on the basis of the interim futility analysis. At day 5, neither the sotrovimab group nor the BRII-196 plus BRII-198 group had significantly higher odds of more favourable outcomes than the placebo group on either the pulmonary scale (adjusted odds ratio sotrovimab 1·07 [95% CI 0·74–1·56]; BRII-196 plus BRII-198 0·98 [95% CI 0·67–1·43]) or the pulmonary-plus complications scale (sotrovimab 1·08 [0·74–1·58]; BRII-196 plus BRII-198 1·00 [0·68–1·46]). By day 90, sustained clinical recovery was seen in 151 (85%) patients in the placebo group compared with 160 (88%) in the sotrovimab group (adjusted rate ratio 1·12 [95% CI 0·91–...

National recommendations for lung cancer screening for former and current smokers aged 55-80 years with a 30-pack-year smoking history create demand to implement efficient and effective systems to offer smoking cessation on a large scale. These older, high-risk smokers differ from participants in past clinical trials of behavioral and pharmacologic interventions for tobacco dependence. There is a gap in knowledge about how best to design systems to extend reach and treatments to maximize smoking cessation in the context of lung cancer screening. Eight clinical trials, seven funded by the National Cancer Institute and one by the Veterans Health Administration, address this gap and form the SCALE (Smoking Cessation within the Context of Lung Cancer Screening) collaboration. This paper describes methodological issues related to the design of these clinical trials: clinical workflow, participant eligibility criteria, screening indication (baseline or annual repeat screen), assessment content, interest in stopping smoking, and treatment delivery method and dose, all of which will affect tobacco treatment outcomes. Tobacco interventions consider the "teachable moment" offered by lung cancer screening, how to incorporate positive and negative screening results, and coordination of smoking cessation treatment with clinical events associated with lung cancer screening. Unique data elements, such as perceived risk of lung cancer and costs of tobacco treatment, are of interest. Lung cancer screening presents a new and promising opportunity to reduce morbidity and mortality resulting from lung cancer that can be amplified by effective smoking cessation treatment. SCALE teamwork and collaboration promise to maximize knowledge gained from the clinical trials.

Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5 years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include 1) discarding those observations, 2) treating them as non-events, 3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.

Summary The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry.

ORCID IDs: 0000-0002-5459-9579 (D.M.V.); 0000-0002-4186-5698 (M.T.D.). AbstractRationale: Lung transplantation is an accepted and increasingly employed treatment for advanced lung diseases, but the anticipated survival benefit of lung transplantation is poorly understood.Objectives: To determine whether and for which patients lung transplantation confers a survival benefit in the modern era of U.S. lung allocation.Methods: Data on 13,040 adults listed for lung transplantation between May 2005 and September 2011 were obtained from the United Network for Organ Sharing. A structural nested accelerated failure time model was used to model the survival benefit of lung transplantation over time. The effects of patient, donor, and transplant center characteristics on the relative survival benefit of transplantation were examined.Measurements and Main Results: Overall, 73.8% of transplant recipients were predicted to achieve a 2-year survival benefit with lung transplantation. The survival benefit of transplantation varied by native disease group (P = 0.062), with 2-year expected benefit in 39.2 and 98.9% of transplants occurring in those with obstructive lung disease and cystic fibrosis, respectively, and by lung allocation score at the time of transplantation (P , 0.001), with net 2-year benefit in only 6.8% of transplants occurring for lung allocation score less than 32.5 and in 99.9% of transplants for lung allocation score exceeding 40.Conclusions: A majority of adults undergoing transplantation experience a survival benefit, with the greatest potential benefit in those with higher lung allocation scores or restrictive native lung disease or cystic fibrosis. These results provide novel information to assess the expected benefit of lung transplantation at an individual level and to enhance lung allocation policy.