The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8–13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to sieve analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.

In the coronavirus efficacy (COVE) phase 3 clinical trial, vaccine recipients were assessed for neutralizing and binding antibodies as correlates of risk for COVID-19 disease and as correlates of protection. These immune markers were measured at the time of second vaccination and 4 weeks later, with values reported in standardized World Health Organization international units. All markers were inversely associated with COVID-19 risk and directly associated with vaccine efficacy. Vaccine recipients with postvaccination 50% neutralization titers 10, 100, and 1000 had estimated vaccine efficacies of 78% (95% confidence interval, 54 to 89%), 91% (87 to 94%), and 96% (94 to 98%), respectively. These results help define immune marker correlates of protection and may guide approval decisions for messenger RNA (mRNA) COVID-19 vaccines and other COVID-19 vaccines.

Anti-spike IgG binding antibody, anti-receptor binding domain IgG antibody, and pseudovirus neutralizing antibody measurements four weeks post-vaccination were assessed as correlates of risk of moderate to severe-critical COVID-19 outcomes through 83 days post-vaccination and as correlates of protection following a single dose of Ad26.COV2.S COVID-19 vaccine in the placebo-controlled phase of ENSEMBLE, an international, randomized efficacy trial. Each marker had evidence as a correlate of risk and of protection, with strongest evidence for 50% inhibitory dilution (ID50) neutralizing antibody titer. The outcome hazard ratio was 0.49 (95% confidence interval 0.29, 0.81; p=0.006) per 10-fold increase in ID50; vaccine efficacy was 60% (43, 72%) at nonquantifiable ID50 (< 2.7 IU50/ml) and rose to 89% (78, 96%) at ID50 = 96.3 IU50/ml. Comparison of the vaccine efficacy by ID50 titer curves for ENSEMBLE-US, the COVE trial of the mRNA-1273 vaccine, and the COV002-UK trial of the AZD1222 vaccine supported consistency of the ID50 titer correlate of protection across trials and vaccine types.

BACKGROUND: Patients with firearm injuries are at high risk of subsequent arrest and injury following hospital discharge. We sought to evaluate the effect of a 6-month joint hospital- and community-based low-intensity intervention on risk of arrest and injury among patients with firearm injuries. METHODS: We conducted a cluster randomized controlled trial, enrolling patients with firearm injuries who received treatment at Harborview Medical Center, the level 1 trauma center in Seattle, Washington, were 18 years or older at the time of injury, spoke English, were able to provide consent and a method of contact, and lived in one of the five study counties. The intervention consisted of hospital-based motivational interviewing, followed by a 6-month community-based intervention, and multiagency support. The primary outcome was the risk of subsequent arrest. The main secondary outcome was the risk of death or subsequent injury requiring treatment in the emergency department or hospitalization. RESULTS: Neither assignment to or engagement with the intervention, defined as having at least 1 contact point with the support specialist, was associated with risk of arrest at 2 years post-hospital discharge (relative risk for intervention assignment, 1.15; 95% confidence interval, 0.90-1.48; relative risk for intervention engagement, 1.07; 95% confidence interval, 0.74-2.19). There was similarly no association observed for subsequent injury. CONCLUSIONS: This study represents one of the first randomized controlled trials of a joint hospital- and community-based intervention delivered exclusively among patients with firearm injuries. The intervention was not associated with changes in risk of arrest or injury, a finding most likely due to the low intensity of the program. LEVEL OF EVIDENCE: Care management, level II.

Multiple candidate vaccines to prevent COVID-19 have entered large-scale phase 3 placebo-controlled randomized clinical trials, and several have demonstrated substantial short-term efficacy. At some point after demonstration of substantial efficacy, placebo recipients should be offered the efficacious vaccine from their trial, which will occur before longer-term efficacy and safety are known. The absence of a placebo group could compromise assessment of longer-term vaccine effects. However, by continuing follow-up after vaccination of the placebo group, this study shows that placebo-controlled vaccine efficacy can be mathematically derived by assuming that the benefit of vaccination over time has the same profile for the original vaccine recipients and the original placebo recipients after their vaccination. Although this derivation provides less precise estimates than would be obtained by a standard trial where the placebo group remains unvaccinated, this proposed approach allows estimation of longer-term effect, including durability of vaccine efficacy and whether the vaccine eventually becomes harmful for some. Deferred vaccination, if done open-label, may lead to riskier behavior in the unblinded original vaccine group, confounding estimates of long-term vaccine efficacy. Hence, deferred vaccination via blinded crossover, where the vaccine group receives placebo and vice versa, would be the preferred way to assess vaccine durability and potential delayed harm. Deferred vaccination allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain them on placebo, yet still allows important insights about immunologic and clinical effectiveness over time.

The TechStep study is an ongoing randomized controlled trial in HIV-negative transgender youths and young adults, which will evaluate the efficacy of mobile health interventions for reducing risk behaviors. Several mobile interventions are available, which complicates the design. To evaluate different combinations of mHealth interventions, TechStep is utilizing a sequentially randomized design. In this work, we discuss the motivation for this design, propose robust methodology for the analysis of the trial, and evaluate the methodology via simulation.

BACKGROUND A safe, effective vaccine is essential to eradicating human immunodeficiency virus (HIV) infection. A canarypox–protein HIV vaccine regimen (ALVAC-HIV plus AIDSVAX B/E) showed modest efficacy in reducing infection in Thailand. An analogous regimen using HIV-1 subtype C virus showed potent humoral and cellular responses in a phase 1–2a trial in South Africa. Efficacy data and additional safety data were needed for this regimen in a larger population in South Africa. METHODS In this phase 2b–3 trial, we randomly assigned 5404 adults without HIV-1 infection to receive the vaccine (2704 participants) or placebo (2700 participants). The vaccine regimen consisted of injections of ALVAC-HIV at months 0 and 1, followed by four booster injections of ALVAC-HIV plus bivalent subtype C gp120–MF59 adjuvant at months 3, 6, 12, and 18. The primary efficacy outcome was the occurrence of HIV-1 infection from randomization to 24 months. RESULTS In January 2020, prespecified criteria for nonefficacy were met at an interim analysis; further vaccinations were subsequently halted. The median age of the trial participants was 24 years; 70% of the participants were women. The incidence of adverse events was similar in the vaccine and placebo groups. During the 24-month followup, HIV-1 infection was diagnosed in 138 participants in the vaccine group and in 133 in the placebo group (hazard ratio, 1.02; 95% confidence interval, 0.81 to 1.30; P=0.84). CONCLUSIONS The ALVAC–gp120 regimen did not prevent HIV-1 infection among participants in South Africa despite previous evidence of immunogenicity.

When predicting an outcome is the scientific goal, one must decide on a metric by which to evaluate the quality of predictions. We consider the problem of measuring the performance of a prediction algorithm with the same data that were used to train the algorithm. Typical approaches involve bootstrapping or cross-validation. However, we demonstrate that bootstrap-based approaches often fail and standard cross-validation estimators may perform poorly. We provide a general study of cross-validation-based estimators that highlights the source of this poor performance, and propose an alternative framework for estimation using techniques from the efficiency theory literature. We provide a theorem establishing the weak convergence of our estimators. The general theorem is applied in detail to two specific examples and we discuss possible extensions to other parameters of interest. For the two explicit examples that we consider, our estimators demonstrate remarkable finite-sample improvements over standard approaches. Supplementary materials for this article are available online.