IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v5y2009i1n3.html
   My bibliography  Save this article

Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression

Author

Listed:
  • Hanley James A

    (McGill University)

  • Miettinen Olli S

    (McGill University)

Abstract

When considering treatment options, a physician ideally has access to prognoses for various spans of prospective time, meaning known risks specific for these and also for both treatment and the profile of the patient. Accordingly, investigators ideally would report estimates of such risks from clinical trials and their non-experimental counterparts. To the extent that such risk estimates have been reported at all, they have mainly been based on the semi-parametric regression model of Cox. We focus on a family of fully-parametric hazard models of an attractive, versatile form that readily allows for non-proportionality, yet models that have not been easy to fit with standard statistical software. We elaborate an approach, recently proposed, to fitting such hazard functions via logistic regression. From the fitted hazard function, cumulative incidence and, thus, risk functions of time, treatment and profile can be derived. This approach accommodates any log-linear hazard function of prognostic time, treatment, and the prognostic indicators defining the patient's prognostic profile.

Suggested Citation

  • Hanley James A & Miettinen Olli S, 2009. "Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, January.
    • Handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:3
    DOI: 10.2202/1557-4679.1125
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1125
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1125?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. Murray Aitkin & David Clayton, 1980. "The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 156-163, June.
    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. Olli Saarela & James A. Hanley, 2015. "Case-base methods for studying vaccination safety," Biometrics, The International Biometric Society, vol. 71(1), pages 42-52, March.
    2. Olli Saarela & Elja Arjas, 2015. "Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 609-626, June.
    3. Olli Saarela, 2016. "A case-base sampling method for estimating recurrent event intensities," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 589-605, October.

    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. Powers, Daniel A. & Yun, Myeong-Su, 2009. "Multivariate Decomposition for Hazard Rate Models," IZA Discussion Papers 3971, Institute of Labor Economics (IZA).
    2. Orbe, Jesus & Nunez-Anton, Vicente, 2006. "Alternative approaches to study lifetime data under different scenarios: from the PH to the modified semiparametric AFT model," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1565-1582, March.
    3. A. J. Scallan, 1999. "Regression modelling of interval-censored failure time data using the Weibull distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(5), pages 613-618.
    4. Trond Petersen, 1986. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates," Sociological Methods & Research, , vol. 14(3), pages 219-246, February.
    5. Orbe Lizundia, Jesús María, 2000. "Un Modelo Lineal Generalizado Semiparametrico para Analisis de Duracion con Censura," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    6. M. M. Shoukri & M. Attanasio & J. M. Sargeant, 1998. "Parametric versus semi-parametric models for the analysis of correlated survival data: A case study in veterinary epidemiology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(3), pages 357-374.
    7. Irit Aitkin, "undated". "The effect of missing data on covariates in survival analysis," Australasian Stata Users' Group Meetings 2004 6, Stata Users Group.
    8. A. J. Scallan, 1999. "Fitting a mixture distribution to complex censored survival data using generalized linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 747-753.
    9. Thomas R. Fleming & D. Y. Lin, 2000. "Survival Analysis in Clinical Trials: Past Developments and Future Directions," Biometrics, The International Biometric Society, vol. 56(4), pages 971-983, December.
    10. Fuentes, R. & Dwyer-Joyce, R.S. & Marshall, M.B. & Wheals, J. & Cross, E.J., 2020. "Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling," Renewable Energy, Elsevier, vol. 147(P1), pages 776-797.

    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:bpj:ijbist:v:5:y:2009:i:1:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.