IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v33y2024i1d10.1007_s10260-024-00744-2.html
   My bibliography  Save this article

Determining sample sizes for combined incident and prevalent cohort studies with and without follow-up

Author

Listed:
  • James H. McVittie

    (University of Regina)

Abstract

The determination of the sample size is key in the design of a cohort study when requiring a preset statistical power for comparing time to event outcomes of two groups. In complex survival analysis study designs, the time to event data for the two groups can be sampled from a single cohort using a variety of different procedures or, the time to event data can be drawn from a collection of different cohorts. By assuming a unified study design where the observations from various sampling schemes or independent cohort studies are combined, the potential logistical constraints on acquiring a sufficient number of subjects may be mitigated. We derive sample size formulae for data collected from combined incident and prevalent cohort studies with and without follow-up. We show analytically how a combined cohort study requires fewer observations from its individual cohort components relative to studies using data collected solely from a single cohort. We describe how our sample size formulae may be generalized to arbitrary collections of cohort samples and demonstrate, using simulated cohort data, how the proposed combined cohort testing procedure achieves comparable empirical power relative to when the same procedure is applied to data drawn from a single cohort study.

Suggested Citation

  • James H. McVittie, 2024. "Determining sample sizes for combined incident and prevalent cohort studies with and without follow-up," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 303-323, March.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:1:d:10.1007_s10260-024-00744-2
    DOI: 10.1007/s10260-024-00744-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-024-00744-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-024-00744-2?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. Welch, Shawn M., 1998. "Nonparametric estimates of the duration of welfare spells," Economics Letters, Elsevier, vol. 60(2), pages 217-221, August.
    2. Jing Ning & Jing Qin & Yu Shen, 2010. "Non‐parametric tests for right‐censored data with biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 609-630, November.
    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. James H. McVittie & Ana F. Best & David B. Wolfson & David A. Stephens & Julian Wolfson & David L. Buckeridge & Shahinaz M. Gadalla, 2023. "Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records," International Statistical Review, International Statistical Institute, vol. 91(1), pages 72-87, April.
    2. James H. McVittie & David B. Wolfson & David A. Stephens, 2023. "A note on the partial likelihood estimator of the proportional hazards model for combined incident and prevalent cohort data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(4), pages 487-497, May.
    3. Jue Hou & Christina D. Chambers & Ronghui Xu, 2018. "A nonparametric maximum likelihood approach for survival data with observed cured subjects, left truncation and right-censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 612-651, October.
    4. Jing Ning & Jing Qin & Yu Shen, 2014. "Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1625-1635, December.
    5. Yu Shen & Jing Ning & Jing Qin, 2017. "Nonparametric and semiparametric regression estimation for length-biased survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 3-24, January.

    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:spr:stmapp:v:33:y:2024:i:1:d:10.1007_s10260-024-00744-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.