IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v37y2010i12p2057-2071.html
   My bibliography  Save this article

Notes on odds ratio estimation for a randomized clinical trial with noncompliance and missing outcomes

Author

Listed:
  • Kung-Jong Lui
  • Kuang-Chao Chang

Abstract

The odds ratio (OR) has been recommended elsewhere to measure the relative treatment efficacy in a randomized clinical trial (RCT), because it possesses a few desirable statistical properties. In practice, it is not uncommon to come across an RCT in which there are patients who do not comply with their assigned treatments and patients whose outcomes are missing. Under the compound exclusion restriction, latent ignorable and monotonicity assumptions, we derive the maximum likelihood estimator (MLE) of the OR and apply Monte Carlo simulation to compare its performance with those of the other two commonly used estimators for missing completely at random (MCAR) and for the intention-to-treat (ITT) analysis based on patients with known outcomes, respectively. We note that both estimators for MCAR and the ITT analysis may produce a misleading inference of the OR even when the relative treatment effect is equal. We further derive three asymptotic interval estimators for the OR, including the interval estimator using Wald's statistic, the interval estimator using the logarithmic transformation, and the interval estimator using an ad hoc procedure of combining the above two interval estimators. On the basis of a Monte Carlo simulation, we evaluate the finite-sample performance of these interval estimators in a variety of situations. Finally, we use the data taken from a randomized encouragement design studying the effect of flu shots on the flu-related hospitalization rate to illustrate the use of the MLE and the asymptotic interval estimators for the OR developed here.

Suggested Citation

  • Kung-Jong Lui & Kuang-Chao Chang, 2010. "Notes on odds ratio estimation for a randomized clinical trial with noncompliance and missing outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2057-2071.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2057-2071
    DOI: 10.1080/02664760903214411
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903214411
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760903214411?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. Lui, Kung-Jong & Cumberland, William G., 2008. "Notes on estimation of proportion ratio under a non-compliance randomized trial with missing outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4325-4345, May.
    2. Shigeyuki Matsui, 2005. "Stratified Analysis in Randomized Trials with Noncompliance," Biometrics, The International Biometric Society, vol. 61(3), pages 816-823, September.
    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. Lui, Kung-Jong & Chang, Kuang-Chao, 2009. "Interval estimation of odds ratio in a stratified randomized clinical trial with noncompliance," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2754-2766, May.
    2. Bandyopadhyay, Uttam & Sarkar, Suman & Biswas, Atanu, 2022. "Sequential confidence interval for comparing two Bernoulli distributions in a non-conventional set-up," Statistics & Probability Letters, Elsevier, vol. 181(C).
    3. Lui, Kung-Jong, 2008. "Notes on interval estimation of risk difference in stratified noncompliance randomized trials: A Monte Carlo evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 4091-4103, April.
    4. Lui, Kung-Jong & Chang, Kuang-Chao, 2008. "Testing homogeneity of risk difference in stratified randomized trials with noncompliance," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 209-221, September.
    5. Lui, Kung-Jong & Cumberland, William G., 2008. "Notes on estimation of proportion ratio under a non-compliance randomized trial with missing outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4325-4345, May.
    6. Ali Reza Soltanian & Soghrat Faghihzadeh, 2012. "A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1037-1048, October.

    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:japsta:v:37:y:2010:i:12:p:2057-2071. 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/CJAS20 .

    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.