IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i2p1293-1305.html
   My bibliography  Save this article

Power analysis for cluster randomized trials with continuous coprimary endpoints

Author

Listed:
  • Siyun Yang
  • Mirjam Moerbeek
  • Monica Taljaard
  • Fan Li

Abstract

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K (K≥2$K\ge 2$) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed‐form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation‐maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.

Suggested Citation

  • Siyun Yang & Mirjam Moerbeek & Monica Taljaard & Fan Li, 2023. "Power analysis for cluster randomized trials with continuous coprimary endpoints," Biometrics, The International Biometric Society, vol. 79(2), pages 1293-1305, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1293-1305
    DOI: 10.1111/biom.13692
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13692
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13692?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fan Li & Elizabeth L. Turner & John S. Preisser, 2018. "Sample size determination for GEE analyses of stepped wedge cluster randomized trials," Biometrics, The International Biometric Society, vol. 74(4), pages 1450-1458, December.
    2. Anindya Roy & Dulal K. Bhaumik & Subhash Aryal & Robert D. Gibbons, 2007. "Sample Size Determination for Hierarchical Longitudinal Designs with Differential Attrition Rates," Biometrics, The International Biometric Society, vol. 63(3), pages 699-707, September.
    3. Leiva, Ricardo, 2007. "Linear discrimination with equicorrelated training vectors," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 384-409, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ricardo Leiva & Anuradha Roy, 2016. "Multi-level multivariate normal distribution with self-similar compound symmetry covariance matrix," Working Papers 0146mss, College of Business, University of Texas at San Antonio.
    2. Roy, Anuradha & Leiva, Ricardo & Žežula, Ivan & Klein, Daniel, 2015. "Testing the equality of mean vectors for paired doubly multivariate observations in blocked compound symmetric covariance matrix setup," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 50-60.
    3. Anuradha Roy & Ricardo Leiva, 2013. "Testing the Equality of Mean Vectors for Paired Doubly Multivariate Observations," Working Papers 0180mss, College of Business, University of Texas at San Antonio.
    4. Kihoon Yoon & Daijin Ko & Carolina B. Livi & Nathan Trinklein & Mark Doderer & Stephen Kwek & Luiz O. F. Penalva, 2008. "Over-represented sequences located on UTRs are potentially involved in regulatory functions," Working Papers 0053, College of Business, University of Texas at San Antonio.
    5. Moonseong Heo & Andrew C. Leon, 2008. "Statistical Power and Sample Size Requirements for Three Level Hierarchical Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 64(4), pages 1256-1262, December.
    6. Anup Amatya & Dulal K. Bhaumik, 2018. "Sample size determination for multilevel hierarchical designs using generalized linear mixed models," Biometrics, The International Biometric Society, vol. 74(2), pages 673-684, June.
    7. Roy, Anuradha & Zmyślony, Roman & Fonseca, Miguel & Leiva, Ricardo, 2016. "Optimal estimation for doubly multivariate data in blocked compound symmetric covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 81-90.
    8. Timothy Opheim & Anuradha Roy, 2021. "Linear models for multivariate repeated measures data with block exchangeable covariance structure," Computational Statistics, Springer, vol. 36(3), pages 1931-1963, September.
    9. Katarzyna Filipiak & Mateusz John & Daniel Klein, 2023. "Testing independence under a block compound symmetry covariance structure," Statistical Papers, Springer, vol. 64(2), pages 677-704, April.
    10. Anuradha Roy, 2010. "Linear Models for Multivariate Repeated Measures Data," Working Papers 0017, College of Business, University of Texas at San Antonio.
    11. Kendra Davis‐Plourde & Monica Taljaard & Fan Li, 2023. "Sample size considerations for stepped wedge designs with subclusters," Biometrics, The International Biometric Society, vol. 79(1), pages 98-112, March.
    12. Tatjana Pavlenko & Anuradha Roy, 2013. "Supervised classifiers of ultra high-dimensional higher-order data with locally doubly exchangeable covariance structure," Working Papers 0185mss, College of Business, University of Texas at San Antonio.
    13. Hao, Chengcheng & Liang, Yuli & Roy, Anuradha, 2015. "Equivalency between vertices and centers-coupled-with-radii principal component analyses for interval data," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 113-120.
    14. Heo, Moonseong & Xue, Xiaonan & Kim, Mimi Y., 2013. "Sample size requirements to detect an intervention by time interaction in longitudinal cluster randomized clinical trials with random slopes," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 169-178.
    15. Amitrajeet A. Batabyal & Hamid Beladi, 2015. "Optimal Transport Provision To A Tourist Destination: A Mechanism Design Approach," Working Papers 0141mss, College of Business, University of Texas at San Antonio.
    16. Satoshi Usami, 2017. "Generalized SAMPLE SIZE Determination Formulas for Investigating Contextual Effects by a Three-Level Random Intercept Model," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 133-157, March.
    17. Roy, Anuradha & Leiva, Ricardo, 2008. "Likelihood ratio tests for triply multivariate data with structured correlation on spatial repeated measurements," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1971-1980, September.
    18. Leiva, Ricardo & Roy, Anuradha, 2012. "Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1644-1661.
    19. Anuradha Roy & Ricardo Leiva, 2008. "An Extension of the Traditional Classi cation Rules: the Case of Non-Random Samples," Working Papers 0057, College of Business, University of Texas at San Antonio.
    20. Dateng Li & Jing Cao & Song Zhang, 2020. "Power analysis for cluster randomized trials with multiple binary co‐primary endpoints," Biometrics, The International Biometric Society, vol. 76(4), pages 1064-1074, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1293-1305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.