IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v109y2014i507p931-943.html
   My bibliography  Save this article

Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome

Author

Listed:
  • Peter F. Thall
  • Hoang Q. Nguyen
  • Sarah Zohar
  • Pierre Maton

Abstract

The intubation-surfactant-extubation (INSURE) procedure is used worldwide to treat preterm newborn infants suffering from respiratory distress syndrome, which is caused by an insufficient amount of the chemical surfactant in the lungs. With INSURE, the infant is intubated, surfactant is administered via the tube to the trachea, and at completion the infant is extubated. This improves the infant's ability to breathe and thus decreases the risk of long-term neurological or motor disabilities. To perform the intubation safely, the newborn infant first must be sedated. Despite extensive experience with INSURE, there is no consensus on what sedative dose is best. This article describes a Bayesian sequentially adaptive design for a multi-institution clinical trial to optimize the sedative dose given to preterm infants undergoing the INSURE procedure. The design is based on three clinical outcomes, two efficacy and one adverse, using elicited numerical utilities of the eight possible elementary outcomes. A flexible Bayesian parametric trivariate dose-outcome model is assumed, with the prior derived from elicited mean outcome probabilities. Doses are chosen adaptively for successive cohorts of infants using posterior mean utilities, subject to safety and efficacy constraints. A computer simulation study of the design is presented. Supplementary materials for this article are available online.

Suggested Citation

  • Peter F. Thall & Hoang Q. Nguyen & Sarah Zohar & Pierre Maton, 2014. "Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 931-943, September.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:507:p:931-943
    DOI: 10.1080/01621459.2014.904789
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2014.904789
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2014.904789?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nigel Stallard & Peter F. Thall, 2001. "Decision-Theoretic Designs for Pre-Phase II Screening Trials in Oncology," Biometrics, The International Biometric Society, vol. 57(4), pages 1089-1095, December.
    2. Nigel Stallard & Peter F. Thall & John Whitehead, 1999. "Decision Theoretic Designs for Phase II Clinical Trials with Multiple Outcomes," Biometrics, The International Biometric Society, vol. 55(3), pages 971-977, September.
    3. Simas, Alexandre B. & Barreto-Souza, Wagner & Rocha, Andréa V., 2010. "Improved estimators for a general class of beta regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 348-366, February.
    4. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    5. Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
    6. David Azriel & Micha Mandel & Yosef Rinott, 2010. "The Treatment Versus Experimentation Dilemma in Dose-finding Studies," Discussion Paper Series dp559, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    7. Peter F. Thall & Aniko Szabo & Hoang Q. Nguyen & Catherine M. Amlie-Lefond & Osama O. Zaidat, 2011. "Optimizing the Concentration and Bolus of a Drug Delivered by Continuous Infusion," Biometrics, The International Biometric Society, vol. 67(4), pages 1638-1646, December.
    8. Dette, Holger & Bretz, Frank & Pepelyshev, Andrey & Pinheiro, José, 2008. "Optimal Designs for Dose-Finding Studies," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1225-1237.
    9. Denis Heng‐Yan Leung & You‐Gan Wang, 2001. "A Bayesian Decision Approach for Sample Size Determination in Phase II Trials," Biometrics, The International Biometric Society, vol. 57(1), pages 309-312, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peter F. Thall & Hoang Q. Nguyen & Ralph G. Zinner, 2017. "Parametric dose standardization for optimizing two-agent combinations in a phase I–II trial with ordinal outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 201-224, January.

    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. Nigel Stallard, 2003. "Decision-Theoretic Designs for Phase II Clinical Trials Allowing for Competing Studies," Biometrics, The International Biometric Society, vol. 59(2), pages 402-409, June.
    2. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    3. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.
    4. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    5. Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.
    6. Diego Ramos Canterle & Fábio Mariano Bayer, 2019. "Variable dispersion beta regressions with parametric link functions," Statistical Papers, Springer, vol. 60(5), pages 1541-1567, October.
    7. Yury R. Benites & Vicente G. Cancho & Edwin M. M. Ortega & Roberto Vila & Gauss M. Cordeiro, 2022. "A New Regression Model on the Unit Interval: Properties, Estimation, and Application," Mathematics, MDPI, vol. 10(17), pages 1-17, September.
    8. Giovanna Bua & Carmine Trecroci, 2019. "International equity markets interdependence: bigger shocks or contagion in the 21st century?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 155(1), pages 43-69, February.
    9. Francisco Cribari-Neto & Sadraque E.F. Lucena, 2015. "Nonnested hypothesis testing in the class of varying dispersion beta regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 967-985, May.
    10. Frank A. La Sorte & Alison Johnston & Toby R. Ault, 2021. "Global trends in the frequency and duration of temperature extremes," Climatic Change, Springer, vol. 166(1), pages 1-14, May.
    11. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    12. Emilio Gómez-Déniz & Jorge V Pérez-Rodríguez & José Boza-Chirino, 2020. "Modelling tourist expenditure at origin and destination," Tourism Economics, , vol. 26(3), pages 437-460, May.
    13. Edilberto Cepeda-Cuervo & Jorge Alberto Achcar & Liliana Garrido Lopera, 2014. "Bivariate beta regression models: joint modeling of the mean, dispersion and association parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 677-687, March.
    14. Phillip Li, 2018. "Efficient MCMC estimation of inflated beta regression models," Computational Statistics, Springer, vol. 33(1), pages 127-158, March.
    15. Ospina, Raydonal & Ferrari, Silvia L.P., 2012. "A general class of zero-or-one inflated beta regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1609-1623.
    16. Peter F. Thall & Hoang Q. Nguyen & Ralph G. Zinner, 2017. "Parametric dose standardization for optimizing two-agent combinations in a phase I–II trial with ordinal outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 201-224, January.
    17. Fábio Bayer & Francisco Cribari-Neto, 2015. "Bootstrap-based model selection criteria for beta regressions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 776-795, December.
    18. Chen, Kee Kuo & Chiu, Rong-Her & Chang, Ching-Ter, 2017. "Using beta regression to explore the relationship between service attributes and likelihood of customer retention for the container shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 1-16.
    19. Yiyun Shou & Michael Smithson, 2015. "Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 236-256, March.
    20. Patrícia L. Espinheira & Alisson Oliveira Silva, 2020. "Residual and influence analysis to a general class of simplex regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 523-552, June.

    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:taf:jnlasa:v:109:y:2014:i:507:p:931-943. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.