Confidence Intervals and Sample Size Calculations for Studies of Film-reading Performance

IntroductionPatients with cancer undergoing surgery often suffer from reduced quality of life and various forms of distress. Untreated distress can negatively affect coping resources as well as surgical and oncological outcomes. A virtual reality-based stress reduction intervention may increase quality of life and well-being and reduce distress in the perioperative phase for patients with cancer. This pilot trial aims to explore the feasibility of the proposed intervention, assess patient acceptability and obtain estimates of effect to provide data for sample size calculations.Methods and analysisPatients with colorectal cancer and liver metastasis undergoing elective surgery will be recruited for this single-centre, randomised pilot trial with a three-arm design. A total of 54 participants will be randomised at 1:1:1 ratio to one of two intervention groups or a control receiving standard treatment. Those randomised to an intervention group will either receive perioperative virtual reality-based stress reduction exercises twice daily or listen to classical music twice daily. Primary feasibility outcomes are number and proportions of participants recruited, screened, consented and randomised. Furthermore, adherence to the intervention, compliance with the completion of the quality of life questionnaires and feasibility of implementing the trial procedures will be assessed. Secondary clinical outcomes are measurements of the effectiveness of the interventions to inform sample size calculations.Ethics and disseminationThe study protocol, the patient information and the informed consent form have been approved by the ethics committee of the Ludwigs-Maximilians-University, Munich, Germany (Reference Number: 19–915). Study findings will be submitted for publication in peer-reviewed journals.Trial registration numberDRKS00020909.

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Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials

Clinical Trials ◽

10.1177/17407745211020852 ◽

2021 ◽

pp. 174077452110208

Author(s):

Elizabeth Korevaar ◽

Jessica Kasza ◽

Monica Taljaard ◽

Karla Hemming ◽

Terry Haines ◽

...

Keyword(s):

Sample Size ◽

Discrete Time ◽

Correlation Coefficients ◽

Time Decay ◽

Randomised Trials ◽

Cluster Randomised Trials ◽

Cluster Randomised ◽

Sample Size Calculations ◽

Correlation Structures ◽

Cluster Correlation

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

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