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Survival vs. mortality results from cancer detection

March 13, 2007  |  Edward Tufte
5 Comment(s)

Many new medical interventions look good when evaluated by survival time but fail when
evaluated by mortality rates. See a fine article in The New York Times

here, an article that would appear in no other newspaper.

Alvin
Feinstein, my colleague at Yale, made the point for years that early diagnosis makes
treatments look good because early diagnosis leads to longer survival times–but often with
no improvement in mortality rates.

Topics: E.T.
Comments
  • Niels Olson says:
    March 13, 2007 at 1:59 pm

    I think these two paragraphs should have been further up in the article:

    Imagine two patients with lung cancer. Even if both die at age 70, a patient with cancer diagnosed by spiral CT screening at age 59 has a longer survival than one with cancer diagnosed because of symptoms (cough, weight loss and so on) at age 67. The first patient survives 11 years; the second 3 years. But both died at the same age. Survival is increased, but mortality is the same.

    A second source of distortion results from overdiagnosis, when screening finds cancers that were never destined to progress and cause death. Overdiagnosis bias can also drastically inflate survival statistics, even if mortality is unchanged.

    The author follows these excellent illustrations by saying “To understand why, you need to understand the definition of the two statistics” but never provides the definitions. If NYT provided definition links to Wikipedia, instead of their own search engine, this omission could have been nicely resolved. The reader’s state of awareness goes from vague to clear, and back to vague. Instead of using the PGP model, the author has used a GPG model.[PGP: Particular—General—Particular. Frederick Mosteller, “Classroom and Platform Performance“, The American Statistician, February 1980, Vol. 34, No.1, pp. 11-17.]

    So many benign, slow-growing adrenal tumors are found on CT that some have taken to calling them incidentalomas. There is a similar observation about a form of breast cancer, ductal carcinoma in situ: many die with it, few die of it.

  • Chris Pounds says:
    March 15, 2007 at 12:50 pm

    If a treatment is only symptomatic then early or late diagnosis may not matter at all. If the therapy treats disease progression, then early treatment may make a much larger difference on the survival time. Communicating this concept is often done with graphs that show a decline in status and the “catch-up” effect with a symptomatic therapy.

    Cancer therapies often use biomarkers to link their drug to something critical (like survival time), but diseases of the central nervous system rarely have these markers.

  • Sherman Dorn says:
    June 10, 2007 at 7:13 am

    For an interesting analysis of non-cohort survival measures and distortions caused by changes in diagnosis numbers, see Samuel H. Preston, “Relations among Standard Epidemiologic Measures in a Population,” American Journal of Epidemiology, 126 (1987): 336-45.

  • ET says:
    June 10, 2007 at 9:21 pm

    Can a Kindly Contributor find a link to this article?

    Thanks

    ET

  • Troy Goodson says:
    June 12, 2007 at 4:27 pm

    http://aje.oxfordjournals.org/cgi/content/abstract/126/2/336

    RELATIONS AMONG STANDARD EPIDEMIOLOGIC MEASURES IN A POPULATION

    SAMUEL H. PRESTON
    Population Studies Center, 3718 Locust Wallc/CR, University of Pennsylvania, Philadelphia,
    PA 19104. (Send reprint requests to Dr. Samuel H.Preston at this address.)

    Recent developments In population mathematics apply to measurement issues in
    epidemiology. In particular, they demonstrate explicitly the relations that prevail among
    incidence, prevalence, case-fatality, mortality, and duration of illness in a population at a
    moment in time, rather than in a cohort of persons followed through time (or in a
    population artificially assumed to be stationary). They indicate explicitly how certain
    common indicators such as the ratio of deaths to new cases should be interpreted. They
    also suggest possible new strategies for estimating certain measures, but these would
    require some reorientation of current approaches to measurement.

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