Dataset Information

Two Questions About the Design of Cluster Randomized Trials: A Tutorial.

ABSTRACT: This is a short tutorial on two key questions that pertain to cluster randomized trials (CRTs): 1) Should I perform a CRT? and 2) If so, how do I derive the sample size? In summary, a CRT is the best option when you "must" (e.g., the intervention can only be administered to a group) or you "should" (e.g., because of issues such as feasibility and contamination). CRTs are less statistically efficient and usually more logistically complex than individually randomized trials, and so reviewing the rationale for their use is critical. The most straightforward approach to the sample size calculation is to first perform the calculation as if the design were randomized at the level of the patient and then to inflate this sample size by multiplying by the "design effect", which quantifies the degree to which responses within a cluster are similar to one another. Although trials with large numbers of small clusters are more statistically efficient than those with a few large clusters, trials with large clusters can be more feasible. Also, if results are to be compared across individual sites, then sufficient sample size will be required to attain adequate precision within each site. Sample size calculations should include sensitivity analyses, as inputs from the literature can lack precision. Collaborating with a statistician is essential. To illustrate these points, we describe an ongoing CRT testing a mobile-based app to systematically engage families of intensive care unit patients and help intensive care unit clinicians deliver needs-targeted palliative care.

SUBMITTER: Samsa GP

PROVIDER: S-EPMC8009809 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Two Questions About the Design of Cluster Randomized Trials: A Tutorial.

Samsa Gregory P GP Winger Joseph G JG Cox Christopher E CE Olsen Maren K MK

Journal of pain and symptom management 20201125 4

This is a short tutorial on two key questions that pertain to cluster randomized trials (CRTs): 1) Should I perform a CRT? and 2) If so, how do I derive the sample size? In summary, a CRT is the best option when you "must" (e.g., the intervention can only be administered to a group) or you "should" (e.g., because of issues such as feasibility and contamination). CRTs are less statistically efficient and usually more logistically complex than individually randomized trials, and so reviewing the r ...[more]

PMID: 33246075

Similar Datasets

Project description:In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention, the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks-in particular the degree of connection across randomized units, which can be obtained at study baseline-we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects.We several designs for cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a "holdback" version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial.In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but "holdback" versions provide power that is very close to that of a traditional Stepped Wedge approach.Incorporating information about cluster connectivity in the design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks-by minimizing the number of cross-cluster infections-with very modest cost in terms of power to detect effectiveness.

Project description:Background Cluster randomized trials have been utilized to evaluate the effectiveness of HIV prevention strategies on reducing incidence. Design of such studies must take into account possible correlation of outcomes within randomized units. Purpose To discuss power and sample size considerations for cluster randomized trials of combination HIV prevention, using an HIV prevention study in Botswana as an illustration. Methods We introduce a new agent-based model to simulate the community-level impact of a combination prevention strategy and investigate how correlation structure within a community affects the coefficient of variation - an essential parameter in designing a cluster randomized trial. Results We construct collections of sexual networks and then propagate HIV on them to simulate the disease epidemic. Increasing level of sexual mixing between intervention and standard-of-care (SOC) communities reduces the difference in cumulative incidence in the two sets of communities. Fifteen clusters per arm and 500 incidence cohort members per community provide 95% power to detect the projected difference in cumulative HIV incidence between SOC and intervention communities (3.93% and 2.34%) at the end of the third study year, using a coefficient of variation 0.25. Although available formulas for calculating sample size for cluster randomized trials can be derived by assuming an exchangeable correlation structure within clusters, we show that deviations from this assumption do not generally affect the validity of such formulas. Limitations We construct sexual networks based on data from Likoma Island, Malawi, and base disease progression on longitudinal estimates from an incidence cohort in Botswana and in Durban as well as a household survey in Mochudi, Botswana. Network data from Botswana and larger sample sizes to estimate rates of disease progression would be useful in assessing the robustness of our model results. Conclusion Epidemic modeling plays a critical role in planning and evaluating interventions for prevention. Simulation studies allow us to take into consideration available information on sexual network characteristics, such as mixing within and between communities as well as coverage levels for different prevention modalities in the combination prevention package.

Dataset Information

Two Questions About the Design of Cluster Randomized Trials: A Tutorial.

Publications

Two Questions About the Design of Cluster Randomized Trials: A Tutorial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets