Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks

2020

Science

144

119

Section: Which Interventions Work Best In a Pandemic?mentioning

confidence: 99%

Which interventions work best in a pandemic?

2020

Science

144

119

“…37 Furthermore, because the direct effects continue and indirect effects may increase on short time scales, the effect size in cRCTs in epidemics can increase over time. 21,22 Eventually, however, the exhaustion of susceptible individuals will lead to a reduction in the effect size as incidence rates become more similar between intervention and control clusters. We explore the effects of the time interval used in our simulations by increasing the lag between intervention and evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…We assume that each exposed individual’s incubation period is drawn from an exponential distribution with a mean of 5.51 days. 30 We match the simulations done elsewhere, 21,22 assuming the mean infectious period across individuals is 5 days (from an exponential rather than a gamma distribution). We assume an approximate negative binomial degree distribution for the network structure of each cluster with a mean of 15 contacts per individual and overdispersion parameter k .…”

Section: Methodsmentioning

confidence: 99%

“…Simulation approaches have been previously used to evaluate the statistical power of cRCTs evaluating vaccination at both individual and cluster scales. 19–22 This allows investigators to size cRCTs to have a desired power to detect a specified true effect size of interest (i.e., reduction in transmission). Although estimators have different properties and interpretations depending on the phase of the epidemic—e.g., by capturing more indirect effects or having less impact when there is more pre-existing immunity— this allows hypothesis tests to be appropriately powered.…”

Section: Introductionmentioning

confidence: 99%

“…Although estimators have different properties and interpretations depending on the phase of the epidemic—e.g., by capturing more indirect effects or having less impact when there is more pre-existing immunity— this allows hypothesis tests to be appropriately powered. 21,22 Here, we build on these results to provide estimates for the number of clusters and number of individuals measured within each cluster needed to test the effectiveness of an NPI, with the aim of bounding the feasibility of such analyses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The required size of cluster randomized trials of non-pharmaceutical interventions in epidemic settings

Sheen

et al. 2021

Preprint

Self Cite

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which non-pharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases, even when there is no true effect. Cluster randomized trials provide a means to conduct valid hypothesis tests of the effect of interventions on community transmission. While they may only require a short duration, they often require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in an outbreak setting are largely undeveloped and the question of whether these designs are practical remains unanswered. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where cluster randomized trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, and then more detailed methods may be used to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or settings with extreme overdispersion of transmission, however, much larger sample sizes are required.

The required size of cluster randomized trials of nonpharmaceutical interventions in epidemic settings

Sheen

et al. 2022

Statistics in Medicine

Self Cite

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which nonpharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases that could erroneously suggest an impact on transmission, even when there is no true effect. Cluster randomized trials permit valid hypothesis tests of the effect of interventions on community transmission. While such trials could be completed in a relatively short period of time, they might require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in outbreak settings are largely undeveloped, leaving unanswered the question of whether these designs are practical. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, followed by more detailed methods to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or when transmission is extremely overdispersed, however, much larger sample sizes are required.