Analysis of multicenter clinical trials with very low event rates

Learning about crash causation from countermeasure evaluation: The example of the Queensland minimum passing distance rule

Close passes by motor vehicles endanger both the safety and comfort of bicycle riders. Governments in many countries have introduced laws requiring drivers to maintain at least a minimum distance between their vehicle and the cyclist they are passing, despite relatively poor understanding of the causes of bicycle overtaking crashes at the time. Queensland was the first state in Australia to introduce such a law, with a two-year trial commencing in April 2014. The data collected during the evaluation of the trial were later analysed to answer two main questions: "Under what circumstances do close passes occur?" and "Why do drivers pass too close?". The first question was largely approached by analysing the video observations of more than 18,000 riders (including 2,000 passing events) at 15 locations on Queensland roads and examining the infrastructure, traffic and road user characteristics that influenced passing distances. The second question was addressed in experimental studies which used the video observations as stimuli. This paper demonstrates how the political need for evaluation of a countermeasure can act as a stimulus for research funding that then allows data collection, analysis and better understanding of crash causation. Logically, introduction of a countermeasure should be based on a rigorous understanding of crash causation. But when this does not occur, evaluation may provide data that can be used to answer questions about crash causation - or at least pose new questions.

Acute and Postacute COVID-19 Outcomes Among Immunologically Naive Adults During Delta vs Omicron Waves

IMPORTANCE

The US arrival of the Omicron variant led to a rapid increase in SARS-CoV-2 infections. While numerous studies report characteristics of Omicron infections among vaccinated individuals or persons with previous infection, comprehensive data describing infections among adults who are immunologically naive are lacking.

OBJECTIVES

To examine COVID-19 acute and postacute clinical outcomes among a well-characterized cohort of unvaccinated and previously uninfected adults who contracted SARS-CoV-2 during the Omicron (BA.1/BA.2) surge, and to compare outcomes with infections that occurred during the Delta wave.

DESIGN, SETTING, AND PARTICIPANTS

This prospective multisite cohort study included community-dwelling adults undergoing high-resolution symptom and virologic monitoring in 8 US states between June 2021 and September 2022. Unvaccinated adults aged 30 to less than 65 years without an immunological history of SARS-CoV-2 who were at high risk of infection were recruited. Participants were followed for up to 48 weeks, submitting regular COVID-19 symptom surveys and nasal swabs for SARS-CoV-2 polymerase chain reaction (PCR) testing. Data were analyzed from May to October 2022.

EXPOSURES

Omicron (BA.1/BA.2 lineages) vs Delta SARS-CoV-2 infection, defined as a positive PCR test result that occurred during a period when the variant represented at least 50% of circulating SARS-CoV-2 variants in the participant's geographic region.

MAIN OUTCOMES AND MEASURE(S)

The main outcomes examined were the prevalence and severity of acute (≤28 days after onset) and postacute (≥5 weeks after onset) symptoms.

RESULTS

Among 274 participants who were immunologically naive (mean [SD] age, 49 [9.7] years; 186 [68%] female; 19 [7%] Hispanic participants; 242 [88%] White participants), 166 (61%) contracted SARS-CoV-2. Of these, 137 infections (83%) occurred during the Omicron-predominant period and 29 infections (17%) occurred during the Delta-predominant period. Asymptomatic infections occurred among 7% (95% CI, 3%-12%) of Omicron-wave infections and 0% (95% CI, 0%-12%) of Delta-wave infections. Health care use among individuals with Omicron-wave infections was 79% (95% CI, 43%-92%) lower relative to individuals with Delta-wave infections (P = .001). Compared with individuals infected during the Delta wave, individuals infected during the Omicron wave also experienced a 56% (95% CI, 26%-74%, P = .004) relative reduction in the risk of postacute symptoms and a 79% (95% CI, 54%-91%, P < .001) relative reduction in the rate of postacute symptoms.

CONCLUSIONS AND RELEVANCE

These findings suggest that among adults who were previously immunologically naive, few Omicron-wave (BA.1/BA.2) and Delta-wave infections were asymptomatic. Compared with individuals with Delta-wave infections, individuals with Omicron-wave infections were less likely to seek health care and experience postacute symptoms.

Acute and Post-Acute COVID-19 Outcomes Among Immunologically Naïve Adults During Delta Versus Omicron Waves

Importance

The U.S. arrival of the Omicron variant led to a rapid increase in SARS-CoV-2 infections. While numerous studies report characteristics of Omicron infections among vaccinated individuals and/or persons with a prior history of infection, comprehensive data describing infections among immunologically naïve adults is lacking.

Objective

To examine COVID-19 acute and post-acute clinical outcomes among a well-characterized cohort of unvaccinated and previously uninfected adults who contracted SARS-CoV-2 during the Omicron (BA.1/BA.2) surge, and to compare outcomes with infections that occurred during the Delta wave.

Design

A prospective cohort undergoing high-resolution symptom and virologic monitoring between June 2021 and September 2022.

Setting

Multisite recruitment of community-dwelling adults in 8 U.S. states.

Participants

Healthy, unvaccinated adults between 30 to 64 years of age without an immunological history of SARS-CoV-2 who were at high-risk of infection were recruited. Participants were followed for up to 48 weeks, submitting regular COVID-19 symptom surveys and nasal swabs for SARS-CoV-2 PCR testing.

Exposures

Omicron (BA.1/BA.2 lineages) versus Delta SARS-CoV-2 infection, defined as a positive PCR that occurred during a period when the variant represented ≥50% of circulating SARS-CoV-2 variants in the participant's geographic region.

Main Outcomes and Measures

The main outcomes examined were the prevalence and severity of acute (≤28 days post-onset) and post-acute (≥5 weeks post-onset) symptoms.

Results

Among 274 immunologically naïve participants, 166 (61%) contracted SARS-CoV-2. Of these, 137 (83%) and 29 (17%) infections occurred during the Omicron- and Delta-predominant periods, respectively. Asymptomatic infections occurred among 6.7% (95% CI: 3.1%, 12.3%) of Omicron cases and 0.0% (95% CI: 0.0%, 11.9%) of Delta cases. Healthcare utilization among Omicron cases was 79% (95% CI: 43%, 92%, P =0.001) lower relative to Delta cases. Relative to Delta, Omicron infections also experienced a 56% (95% CI: 26%, 74%, P =0.004) and 79% (95% CI: 54%, 91%, P <0.001) reduction in the risk and rate of post-acute symptoms, respectively.

Conclusions and Relevance

These findings suggest that among previously immunologically naïve adults, few Omicron (BA.1/BA.2) and Delta infections are asymptomatic, and relative to Delta, Omicron infections were less likely to seek healthcare and experience post-acute symptoms.

Are we leaving money on the table in infertility RCTs? Trialists should statistically adjust for prespecified, prognostic covariates to increase power

Infertility randomized controlled trials (RCTs) are often too small to detect realistic treatment effects. Large observational studies have been proposed as a solution. However, this strategy threatens to weaken the evidence base further, because non-random assignment to treatments makes it impossible to distinguish effects of treatment from confounding factors. Alternative solutions are required. Power in an RCT can be increased by adjusting for prespecified, prognostic covariates when performing statistical analysis, and if stratified randomization or minimization has been used, it is essential to adjust in order to get the correct answer. We present data showing that this simple, free and frequently necessary strategy for increasing power is seldom employed, even in trials appearing in leading journals. We use this article to motivate a pedagogical discussion and provide a worked example. While covariate adjustment cannot solve the problem of underpowered trials outright, there is an imperative to use sound methodology to maximize the information each trial yields.

Planning a method for covariate adjustment in individually randomised trials: a practical guide

BACKGROUND

It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them.

METHODS

Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting.

RESULTS

The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service.

CONCLUSIONS

No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.