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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in International Journal of Epidemiology following peer review. The definitive publisher-authenticated version Robert S McCann, Henk van den Berg, Willem Takken, Amanda G Chetwynd, Emanuele Giorgi, Dianne J Terlouw, Peter J Diggle; Reducing contamination risk in cluster-randomized infectious disease-intervention trials, International Journal of Epidemiology, 47, 1, https://doi.org/10.1093/ije/dyy213 is available online at: https://academic.oup.com/ije/article/47/6/2015/5146518

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Reducing contamination risk in cluster-randomized infectious disease-intervention trials

Research output: Contribution to journalJournal article

E-pub ahead of print

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Reducing contamination risk in cluster-randomized infectious disease-intervention trials. / McCann, Robert S; van den Berg, Henk; Chetwynd, Amanda G; Giorgi, Emanuele; Takken, Willem; Terlouw, Dianne J; Diggle, Peter J.

In: International Journal of Epidemiology, Vol. 47, No. 6, 29.10.2018, p. 2015–2024.

Research output: Contribution to journalJournal article

Harvard

McCann, RS, van den Berg, H, Chetwynd, AG, Giorgi, E, Takken, W, Terlouw, DJ & Diggle, PJ 2018, 'Reducing contamination risk in cluster-randomized infectious disease-intervention trials', International Journal of Epidemiology, vol. 47, no. 6, pp. 2015–2024. https://doi.org/10.1093/ije/dyy213

APA

McCann, R. S., van den Berg, H., Chetwynd, A. G., Giorgi, E., Takken, W., Terlouw, D. J., & Diggle, P. J. (2018). Reducing contamination risk in cluster-randomized infectious disease-intervention trials. International Journal of Epidemiology, 47(6), 2015–2024. https://doi.org/10.1093/ije/dyy213

Vancouver

McCann RS, van den Berg H, Chetwynd AG, Giorgi E, Takken W, Terlouw DJ et al. Reducing contamination risk in cluster-randomized infectious disease-intervention trials. International Journal of Epidemiology. 2018 Oct 29;47(6):2015–2024. https://doi.org/10.1093/ije/dyy213

Author

McCann, Robert S ; van den Berg, Henk ; Chetwynd, Amanda G ; Giorgi, Emanuele ; Takken, Willem ; Terlouw, Dianne J ; Diggle, Peter J. / Reducing contamination risk in cluster-randomized infectious disease-intervention trials. In: International Journal of Epidemiology. 2018 ; Vol. 47, No. 6. pp. 2015–2024.

Bibtex

@article{7ddf9cfa25ef46b3ba2588a878222efd,
title = "Reducing contamination risk in cluster-randomized infectious disease-intervention trials",
abstract = "Background: Infectious disease interventions are increasingly tested using cluster-randomized trials (CRTs). These trial settings tend to involve a set of sampling units, such as villages, whose geographic arrangement may present a contamination risk in treatment exposure. The most widely used approach for reducing contamination in these settings is the so-called fried-egg design, which excludes the outer portion of all available clusters from the primary trial analysis. However, the fried-egg design ignores potential intra-cluster spatial heterogeneity and makes the outcome measure inherently less precise. Whereas the fried-egg design may be appropriate in specific settings, alternative methods to optimize the design of CRTs in other settings are lacking.Methods: We present a novel approach for CRT design that either fully includes or fully excludes available clusters in a defined study region, recognizing the potential for intra-cluster spatial heterogeneity. The approach includes an algorithm that allows investigators to identify the maximum number of clusters that could be included for a defined study region and maintain randomness in both the selection of included clusters and the allocation of clusters to either the treatment group or control group. The approach was applied to the design of a CRT testing the effectiveness of malaria vector-control interventions in southern Malawi.Conclusions: Those planning CRTs to evaluate interventions should consider the approach presented here during trial design. The approach provides a novel framework for reducing the risk of contamination among the CRT randomization units in settings where investigators determine the reduction of contamination risk as a high priority and where intra-cluster spatial heterogeneity is likely. By maintaining randomness in the allocation of clusters to either the treatment group or control group, the approach also permits a randomization-valid test of the primary trial hypothesis.",
author = "McCann, {Robert S} and {van den Berg}, Henk and Chetwynd, {Amanda G} and Emanuele Giorgi and Willem Takken and Terlouw, {Dianne J} and Diggle, {Peter J}",
note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in International Journal of Epidemiology following peer review. The definitive publisher-authenticated version Robert S McCann, Henk van den Berg, Willem Takken, Amanda G Chetwynd, Emanuele Giorgi, Dianne J Terlouw, Peter J Diggle; Reducing contamination risk in cluster-randomized infectious disease-intervention trials, International Journal of Epidemiology, 47, 1, https://doi.org/10.1093/ije/dyy213 is available online at: https://academic.oup.com/ije/article/47/6/2015/5146518",
year = "2018",
month = "10",
day = "29",
doi = "10.1093/ije/dyy213",
language = "English",
volume = "47",
pages = "2015–2024",
journal = "International Journal of Epidemiology",
issn = "0300-5771",
publisher = "NLM (Medline)",
number = "6",

}

RIS

TY - JOUR

T1 - Reducing contamination risk in cluster-randomized infectious disease-intervention trials

AU - McCann, Robert S

AU - van den Berg, Henk

AU - Chetwynd, Amanda G

AU - Giorgi, Emanuele

AU - Takken, Willem

AU - Terlouw, Dianne J

AU - Diggle, Peter J

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in International Journal of Epidemiology following peer review. The definitive publisher-authenticated version Robert S McCann, Henk van den Berg, Willem Takken, Amanda G Chetwynd, Emanuele Giorgi, Dianne J Terlouw, Peter J Diggle; Reducing contamination risk in cluster-randomized infectious disease-intervention trials, International Journal of Epidemiology, 47, 1, https://doi.org/10.1093/ije/dyy213 is available online at: https://academic.oup.com/ije/article/47/6/2015/5146518

PY - 2018/10/29

Y1 - 2018/10/29

N2 - Background: Infectious disease interventions are increasingly tested using cluster-randomized trials (CRTs). These trial settings tend to involve a set of sampling units, such as villages, whose geographic arrangement may present a contamination risk in treatment exposure. The most widely used approach for reducing contamination in these settings is the so-called fried-egg design, which excludes the outer portion of all available clusters from the primary trial analysis. However, the fried-egg design ignores potential intra-cluster spatial heterogeneity and makes the outcome measure inherently less precise. Whereas the fried-egg design may be appropriate in specific settings, alternative methods to optimize the design of CRTs in other settings are lacking.Methods: We present a novel approach for CRT design that either fully includes or fully excludes available clusters in a defined study region, recognizing the potential for intra-cluster spatial heterogeneity. The approach includes an algorithm that allows investigators to identify the maximum number of clusters that could be included for a defined study region and maintain randomness in both the selection of included clusters and the allocation of clusters to either the treatment group or control group. The approach was applied to the design of a CRT testing the effectiveness of malaria vector-control interventions in southern Malawi.Conclusions: Those planning CRTs to evaluate interventions should consider the approach presented here during trial design. The approach provides a novel framework for reducing the risk of contamination among the CRT randomization units in settings where investigators determine the reduction of contamination risk as a high priority and where intra-cluster spatial heterogeneity is likely. By maintaining randomness in the allocation of clusters to either the treatment group or control group, the approach also permits a randomization-valid test of the primary trial hypothesis.

AB - Background: Infectious disease interventions are increasingly tested using cluster-randomized trials (CRTs). These trial settings tend to involve a set of sampling units, such as villages, whose geographic arrangement may present a contamination risk in treatment exposure. The most widely used approach for reducing contamination in these settings is the so-called fried-egg design, which excludes the outer portion of all available clusters from the primary trial analysis. However, the fried-egg design ignores potential intra-cluster spatial heterogeneity and makes the outcome measure inherently less precise. Whereas the fried-egg design may be appropriate in specific settings, alternative methods to optimize the design of CRTs in other settings are lacking.Methods: We present a novel approach for CRT design that either fully includes or fully excludes available clusters in a defined study region, recognizing the potential for intra-cluster spatial heterogeneity. The approach includes an algorithm that allows investigators to identify the maximum number of clusters that could be included for a defined study region and maintain randomness in both the selection of included clusters and the allocation of clusters to either the treatment group or control group. The approach was applied to the design of a CRT testing the effectiveness of malaria vector-control interventions in southern Malawi.Conclusions: Those planning CRTs to evaluate interventions should consider the approach presented here during trial design. The approach provides a novel framework for reducing the risk of contamination among the CRT randomization units in settings where investigators determine the reduction of contamination risk as a high priority and where intra-cluster spatial heterogeneity is likely. By maintaining randomness in the allocation of clusters to either the treatment group or control group, the approach also permits a randomization-valid test of the primary trial hypothesis.

U2 - 10.1093/ije/dyy213

DO - 10.1093/ije/dyy213

M3 - Journal article

VL - 47

SP - 2015

EP - 2024

JO - International Journal of Epidemiology

JF - International Journal of Epidemiology

SN - 0300-5771

IS - 6

ER -