Dataset Information

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials.

ABSTRACT: Power considerations for trials evaluating vaccines against infectious diseases are complicated by indirect protective effects of vaccination. While cluster-randomized controlled trials (cRCTs) are less statistically efficient than individually randomized controlled trials (iRCTs), a cRCT's ability to measure direct and indirect vaccine effects may mitigate the loss of efficiency due to clustering. Within cRCTs, the number and size of clusters affects 3 determinants of power: the effect size being measured, disease incidence, and intracluster correlation. We simulated trials conducted in a collection of small communities to assess how indirect protection and clustering affected the power of cRCTs and iRCTs during an emerging epidemic. Across diverse parameters, we found that within the same trial population, cRCTs were never more powerful than iRCTs, although the difference can be small. We also identified 2 effects that attenuated the loss of cRCT power traditionally associated with increased cluster size. First, if enrollment of fewer, larger clusters was performed to achieve higher vaccine coverage within vaccinated communities, this increased the effect to be measured and, consequently, power. Second, the greater rate of imported transmission in larger communities may increase the attack rate and similarly mitigate loss of power relative to a trial in many, smaller communities.

SUBMITTER: Hitchings MDT

PROVIDER: S-EPMC6070038 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials.

Hitchings Matt D T MDT Lipsitch Marc M Wang Rui R Bellan Steven E SE

American journal of epidemiology 20180801 8

Power considerations for trials evaluating vaccines against infectious diseases are complicated by indirect protective effects of vaccination. While cluster-randomized controlled trials (cRCTs) are less statistically efficient than individually randomized controlled trials (iRCTs), a cRCT's ability to measure direct and indirect vaccine effects may mitigate the loss of efficiency due to clustering. Within cRCTs, the number and size of clusters affects 3 determinants of power: the effect size bei ...[more]

PMID: 29522080

Similar Datasets

Project description:BackgroundA key requirement for a useful power calculation is that the calculation mimic the data analysis that will be performed on the actual data, once that data is observed. Close approximations may be difficult to achieve using analytic solutions, however, and thus Monte Carlo approaches, including both simulation and bootstrap resampling, are often attractive. One setting in which this is particularly true is cluster-randomized trial designs. However, Monte Carlo approaches are useful in many additional settings as well. Calculating power for cluster-randomized trials using analytic or simulation-based methods is frequently unsatisfactory due to the complexity of the data analysis methods to be employed and to the sparseness of data to inform the choice of important parameters in these methods.MethodsWe propose that among Monte Carlo methods, bootstrap approaches are most likely to generate data similar to the observed data. In bootstrap approaches, real data are resampled to build complete data sets based on real data that resemble the data for the intended analyses. In contrast, simulation methods would use the real data to estimate parameters for the data, and would then simulate data using these parameters. We describe means of implementing bootstrap power calculation.ResultsWe demonstrate bootstrap power calculation for a cluster-randomized trial with a censored survival outcome and a baseline observation period.ConclusionsBootstrap power calculation is a natural application of resampling methods. It provides a relatively simple solution to power calculation that is likely to be more accurate than analytic solutions or simulation-based calculations, in the sense that the bootstrap approach does not rely on the assumptions inherent in analytic calculations. This method of calculation has several important strengths. Notably, it is simple to achieve great fidelity to the proposed data analysis method and there is no requirement for parameter estimates, or estimates of their variability, from outside settings. So, for example, for cluster-randomized trials, power calculations need not depend on intracluster correlation coefficient estimates from outside studies. In contrast, bootstrap power calculation requires initial data that resemble data that are to be used in the planned study. We are not aware of bootstrap power calculation being previously proposed or explored for cluster-randomized trials, but it can also be applied for other study designs. We show with a simulation study that bootstrap power calculation can replicate analytic power in cases where analytic power can be accurately calculated. We also demonstrate power calculations for correlated censored survival outcomes in a cluster-randomized trial setting, for which we are unaware of an analytic alternative. The method can easily be used when preliminary data are available, as is likely to be the case when research is performed in health delivery systems or other settings where electronic medical records can be obtained.

Project description:BackgroundThis systematic review evaluated the effectiveness of process interventions (education for clinicians, parent presence, education of parents [before and on day of vaccination], and education of patients on day of vaccination) on reducing vaccination pain, fear, and distress and increasing the use of interventions during vaccination.Design/methodsDatabases were searched using a broad search strategy to identify relevant randomized and quasi-randomized controlled trials. Critical outcomes were pain, fear, distress (when applicable), and use of pain management interventions. Data were extracted according to procedure phase (preprocedure, acute, recovery, combinations of these) and pooled using established methods. Analyses were conducted using standardized mean differences (SMD) and risk ratios (RR).ResultsThirteen studies were included. Results were generally mixed. On the basis of low to very low-quality evidence, the following specific critical outcomes showed significant effects suggesting: (1) clinicians should be educated about vaccine injection pain management (use of interventions: SMD 0.66; 95% confidence interval [CI]: 0.47, 0.85); (2) parents should be present (distress preprocedure: SMD -0.85; 95% CI: -1.35, -0.35); (3) parents should be educated before the vaccination day (use of intervention preprocedure: SMD 0.83; 95% CI: 0.25, 1.41 and RR, 2.08; 95% CI: 1.51, 2.86; distress acute: SMD, -0.35; 95% CI: -0.57, -0.13); (4) parents should be educated on the vaccination day (use of interventions: SMD 1.02; 95% CI: 0.22, 1.83 and RR, 2.42; 95% CI: 1.47, 3.99; distress preprocedure+acute+recovery: SMD -0.48; 95% CI: -0.82, -0.15); and (5) individuals 3 years of age and above should be educated on the day of vaccination (fear preprocedure: SMD -0.67; 95% CI: -1.28, -0.07).ConclusionsEducating individuals involved in the vaccination procedure (clinicians, parents of children being vaccinated; individuals above 3 y of age) is beneficial to increase use of pain management strategies, reduce distress surrounding with vaccination, and to reduce fear. When possible, parent presence is also recommended for children undergoing vaccination.

Dataset Information

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials.

Publications

Competing Effects of Indirect Protection and Clustering on the Power of Cluster-Randomized Controlled Vaccine Trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets