Sample size calculations for cluster randomised crossover trials in Australian and New Zealand intensive care research

Research output: Contribution to journalArticleOtherpeer-review

Abstract

Objective: The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. Data sources: We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. Methods: We show sample size calculations for a twointervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratifi ed design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratifi ed design). Results: The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratifi ed design further reduces the sample size requirement compared with the unstratifi ed design. Conclusions: The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.

Original languageEnglish
Pages (from-to)117-123
Number of pages7
JournalCritical Care and Resuscitation
Volume20
Issue number2
Publication statusPublished - 1 Jun 2018

Cite this

@article{3c967b60c7dd4f81b202ef92b4998acc,
title = "Sample size calculations for cluster randomised crossover trials in Australian and New Zealand intensive care research",
abstract = "Objective: The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. Data sources: We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. Methods: We show sample size calculations for a twointervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratifi ed design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratifi ed design). Results: The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratifi ed design further reduces the sample size requirement compared with the unstratifi ed design. Conclusions: The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.",
author = "Arnup, {Sarah J} and McKenzie, {Joanne E} and David Pilcher and Rinaldo Bellomo and Forbes, {Andrew B}",
year = "2018",
month = "6",
day = "1",
language = "English",
volume = "20",
pages = "117--123",
journal = "Critical Care and Resuscitation",
issn = "1441-2772",
publisher = "Australasian Medical Publishing Co. Pty Ltd. (AMPCo)",
number = "2",

}

TY - JOUR

T1 - Sample size calculations for cluster randomised crossover trials in Australian and New Zealand intensive care research

AU - Arnup, Sarah J

AU - McKenzie, Joanne E

AU - Pilcher, David

AU - Bellomo, Rinaldo

AU - Forbes, Andrew B

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Objective: The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. Data sources: We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. Methods: We show sample size calculations for a twointervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratifi ed design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratifi ed design). Results: The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratifi ed design further reduces the sample size requirement compared with the unstratifi ed design. Conclusions: The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.

AB - Objective: The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. Data sources: We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. Methods: We show sample size calculations for a twointervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratifi ed design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratifi ed design). Results: The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratifi ed design further reduces the sample size requirement compared with the unstratifi ed design. Conclusions: The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.

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