Zhe Chen

In cluster randomized trials, the individual‐average treatment effect (i‐ATE) can be different from the cluster‐average treatment effect (c‐ATE) when informative cluster size (ICS) is present, that is, when treatment effects or participant outcomes depend on cluster size. In such scenarios, mixed‐effects models and generalized estimating equations (GEEs) with exchangeable correlation structure are biased for both the i‐ATE and c‐ATE estimands, whereas GEEs with an independence correlation structure or analyses of cluster‐level summaries are recommended in practice. However, when cluster size is non‐informative, mixed‐effects models and GEEs with exchangeable correlation structure can provide unbiased estimation and notable efficiency gains over other methods. Thus, hypothesis tests for ICS would be useful to assess this key phenomenon under cluster randomization. In this work, we develop model‐based, model‐assisted, and randomization‐based tests for ICS in cluster randomized trials. We conduct simulation studies to examine the operating characteristics of these tests, show they have appropriate Type I error rate control and meaningful power, and contrast them to existing model‐based tests used in the observational study setting. The proposed tests are then applied to data from a recent cluster randomized trial, and practical recommendations for using these tests are discussed.

T cell correlates of COVID-19 risk are poorly defined, particularly in people with HIV (PWH). CoVPN 3008 (NCT05168813) Part A was a prospective 14,002-participant cohort study that administered one or two doses of mRNA-1273 COVID-19 vaccine, depending on baseline SARS-CoV-2 serostatus, in participants (83% PWH) in sub-Saharan Africa. Participants self-monitored for prespecified COVID-19 symptoms, defined by Centers for Disease Control and Prevention symptom criteria. Previous work showed that protection from COVID-19 was superior for hybrid versus vaccine-induced immunity, and that neutralizing antibody titers inversely associated with COVID-19 risk. In the present work, we measured T cell responses to Spike and Nucleocapsid at baseline and 4 weeks post last-vaccination (Peak) in a case-cohort study and assessed them as correlates of risk. In Hybrid Group PWH (n = 22 cases), COVID-19 risk over 6 months decreased with increasing Peak Spike BA.4/5-specific CD4 + T cell responses, from 15.8%, 4.7%, and 0% risk at 0.1%, 0.2%, and 0.3% IFN-γ and/or IL-2 response magnitude, respectively. Including baseline Spike-specific neutralizing antibody titer and Peak CD4 + T cell functionality score in the same model increased the association’s strength (overall p < 0.001). Thus, CD4 + T cell responses are an independent correlate of COVID-19 risk among PWH with pre-existing SARS-CoV-2 immunity.

The traditional model specification of stepped‐wedge cluster‐randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed‐time effects. However, when treatment effects vary over time, the constant effect estimator may be biased. In the general setting of stepped‐wedge cluster‐randomized trials with multiple interventions and additive treatment effects, we derive the expected value of the constant effect estimator under exchangeable within‐cluster correlation structures when the true treatment effects vary across exposure time periods. Applying this result to concurrent and factorial stepped wedge designs, we show that the constant effect estimator converges to a design‐dependent weighted average of exposure‐time‐specific treatment effects, with weights that are generally nonuniform and may not correspond to a natural estimand of interest. Extensive simulation studies reveal that ignoring time heterogeneity can lead to biased estimation and poor coverage of the exposure‐time‐averaged treatment effect. We further examine two models designed to accommodate multiple interventions with time‐varying treatment effects: (1) a time‐varying fixed treatment effect model, which allows treatment effects to vary by exposure time but remain fixed for each time point, and (2) a random treatment effect model, where the time‐varying treatment effects are modeled as random deviations from an overall mean. In the simulations considered in this study, concurrent and factorial designs generally yield comparable power across different effect curve shapes under the time‐varying fixed treatment effect model. Finally, we apply the constant effect model and both time‐varying treatment effect models to data from the Prognosticating Outcomes and Nudging Decisions in the Electronic Health Record (PONDER) trial. All three models indicate a lack of treatment effect for either intervention, though they differ in the precision of their estimates, likely due to variations in modeling assumptions.

People with HIV (PWH) are understudied in COVID-19 vaccine trials, leaving knowledge gaps on whether the identified immune correlates of protection also hold in PWH. CoVPN 3008 (NCT05168813) enrolled predominantly PWH and reported lower COVID-19 incidence for a Hybrid vs. Vaccine Group (baseline SARS-CoV-2-positive and one mRNA-1273 dose vs. negative and two doses). Using case-cohort sampling, antibody markers at enrolment (M0) and four weeks post-final vaccination (Peak) are assessed as immune correlates of COVID-19. For the Hybrid Group [n = 287 (195 PWH)], all M0 markers inversely correlate with COVID-19 through 230 days post-Peak, with 50% inhibitory dilution BA.4/5 neutralizing antibody titer (nAb-ID50 BA.4/5) the strongest and only independent correlate (HR per 10-fold increase=0.46, 95% CI 0.28, 0.75; P = 0.002). For the Vaccine Group [n = 115 (86 PWH)], Peak nAb-ID50 BA.4/5 correlates with reduced COVID-19 risk (1.9%, 1.1%, and 0.3% at titers 10, 100, and 1000 AU/ml) through 92, but not 165, days post-Peak. Using multivariable Cox analysis of binding and nAb, nAb titers predict COVID-19 in PWH. Two doses of a 100-µg Ancestral strain mRNA vaccine in baseline-SARS-CoV-2-negative individuals elicit sufficient cross-reacting Omicron antibodies to reduce COVID-19 incidence for 90 days post-Peak, but viral evolution and waning antibodies abrogate this protection thereafter.