IDEAS home Printed from
MyIDEAS: Login to save this article or follow this journal

Cautions Regarding the Fitting and Interpretation of Survival Curves: Examples from NICE Single Technology Appraisals of Drugs for Cancer

  • Martin Connock

    (West Midlands Health Technology Assessment Collaboration (WMHTAC), Unit of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, West Midlands, UK)

  • Chris Hyde

    (Peninsula School of Medicine Technology Assessment Group (PENTAG), University of Exeter, Exeter, Devon, UK)

  • David Moore

    (West Midlands Health Technology Assessment Collaboration (WMHTAC), Unit of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, West Midlands, UK)

Registered author(s):

    The UK National Institute for Health and Clinical Excellence (NICE) has used its Single Technology Appraisal (STA) programme to assess several drugs for cancer. Typically, the evidence submitted by the manufacturer comes from one short-term randomized controlled trial (RCT) demonstrating improvement in overall survival and/or in delay of disease progression, and these are the pre-eminent drivers of cost effectiveness. We draw attention to key issues encountered in assessing the quality and rigour of the manufacturers' modelling of overall survival and disease progression. Our examples are two recent STAs: sorafenib (Nexavar) for advanced hepatocellular carcinoma, and azacitidine (Vidaza) for higher-risk myelodysplastic syndromes (MDS). The choice of parametric model had a large effect on the predicted treatment-dependent survival gain. Logarithmic models (log-Normal and log-logistic) delivered double the survival advantage that was derived from Weibull models. Both submissions selected the logarithmic fits for their base-case economic analyses and justified selection solely on Akaike Information Criterion (AIC) scores. AIC scores in the azacitidine submission failed to match the choice of the log-logistic over Weibull or exponential models, and the modelled survival in the intervention arm lacked face validity. AIC scores for sorafenib models favoured log-Normal fits; however, since there is no statistical method for comparing AIC scores, and differences may be trivial, it is generally advised that the plausibility of competing models should be tested against external data and explored in diagnostic plots. Function fitting to observed data should not be a mechanical process validated by a single crude indicator (AIC). Projective models should show clear plausibility for the patients concerned and should be consistent with other published information. Multiple rather than single parametric functions should be explored and tested with diagnostic plots. When trials have survival curves with long tails exhibiting few events then the robustness of extrapolations using information in such tails should be tested.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: Pay per view

    File URL:
    Download Restriction: Pay per view

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Springer Healthcare | Adis in its journal PharmacoEconomics.

    Volume (Year): 29 (2011)
    Issue (Month): 10 ()
    Pages: 827-837

    in new window

    Handle: RePEc:wkh:phecon:v:29:y:2011:i:10:p:827-837
    Contact details of provider: Web page:

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:wkh:phecon:v:29:y:2011:i:10:p:827-837. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dave Dustin)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.