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A menu-driven facility for sample-size calculations in cluster randomized controlled trials

Author

Listed:
  • Karla Hemming

    (University of Birmingham)

  • Jen Marsh

    (University of Birmingham)

Abstract

We introduce the Stata menu-driven command clustersampsi, which calculates sample sizes, detectable differences, and power for cluster randomized controlled trials. The command permits continuous, binary, and rate outcomes (with normal approximations) for comparisons of two-sided tests in two equal-sized arms. The command allows for specification of the number of clusters available, or the cluster size, or the average cluster size along with an estimate of the variation of cluster sizes. When the number of clusters available is insufficient to detect the required difference at the prespecified power, clustersampsi will return the minimum number of clusters required under the prespecified design along with the minimum detectable difference and maximum achievable power (both for the prespecified number of clusters). Cluster heterogeneity can be parameterized by using either the intracluster correlation or the coefficient of variation. The command is illustrated via examples. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Karla Hemming & Jen Marsh, 2013. "A menu-driven facility for sample-size calculations in cluster randomized controlled trials," Stata Journal, StataCorp LP, vol. 13(1), pages 114-135, March.
    • Handle: RePEc:tsj:stataj:v:13:y:2013:i:1:p:114-135
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    References listed on IDEAS

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    1. Patrick Royston & Abdel Babiker, 2002. "A menu-driven facility for complex sample size calculation in randomized controlled trials with a survival or a binary outcome," Stata Journal, StataCorp LP, vol. 2(2), pages 151-163, May.
    2. Joanne M Garrett, 2001. "Sample size estimation for cluster designed samples," Stata Technical Bulletin, StataCorp LP, vol. 10(60).
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    Cited by:

    1. Greve, Jane & Heinesen, Eskil, 2015. "Evaluating the impact of a school-based health intervention using a randomized field experiment," Economics & Human Biology, Elsevier, vol. 18(C), pages 41-56.
    2. Rodríguez-Lesmes, Paul & Gutierrez, Luis H. & Urueña-Mejia, Juan Carlos & Ortiz, Andres & Medina Rojas, Ivan & Romero, Mauricio, 2023. "The role of local promoters in helping microentrepreneurs engage in digital business training. The case of Expertienda," Documentos de Trabajo 20902, Universidad del Rosario.
    3. Datta, Souvik & Gulati, Sumeet, 2014. "Utility rebates for ENERGY STAR appliances: Are they effective?," Journal of Environmental Economics and Management, Elsevier, vol. 68(3), pages 480-506.
    4. Fabian Kosse & Ranjita Rajan & Michela Tincani, 2023. "The Persistent Effect of Competition on Prosociality," Rationality and Competition Discussion Paper Series 449, CRC TRR 190 Rationality and Competition.
    5. Yitayew, Asresu & Abdulai, Awudu & Yigezu, Yigezu A. & Deneke, Tilaye T. & Kassie, Girma T., 2021. "Impact of agricultural extension services on the adoption of improved wheat variety in Ethiopia: A cluster randomized controlled trial," World Development, Elsevier, vol. 146(C).
    6. Salvucci, Vincenzo & Santos, Ricardo, 2020. "Vulnerability to Natural Shocks: Assessing the Short-Term Impact on Consumption and Poverty of the 2015 Flood in Mozambique," Ecological Economics, Elsevier, vol. 176(C).
    7. Fabian Kosse & Ranjita Rajan & Michela Tincani, 2023. "The Persistent Effect of Competition on Prosociality," Working Papers 2023-028, Human Capital and Economic Opportunity Working Group.
    8. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    9. Fabian Kosse & Ranjita Rajan & Michela Tincani & Michela Maria Tincani, 2023. "The Persistent Effect of Competition on Prosociality," CESifo Working Paper Series 10779, CESifo.
    10. Kosse, Fabian & Rajan, Ranjita & Tincani, Michela M., 2023. "The Persistent Effect of Competition on Prosociality," IZA Discussion Papers 16595, Institute of Labor Economics (IZA).
    11. Jessica Kasza & Andrew B. Forbes, 2019. "Information content of cluster–period cells in stepped wedge trials," Biometrics, The International Biometric Society, vol. 75(1), pages 144-152, March.
    12. Ardanaz, Martin & Otálvaro-Ramírez, Susana & Scartascini, Carlos, 2023. "Does information about citizen participation initiatives increase political trust?," World Development, Elsevier, vol. 162(C).

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