Back to basics: two useful articles to pass along

The Prostate Cancer Foundation posted two useful and basic articles on its blog site this December that may be useful resources for men concerned about their risk for or newly diagnosed with prostate cancer or monitoring their PSA over time (for any one of all sorts of possible reasons).

The first, by Janet Farrar Worthington, is entitled “Make sure your PSA is as accurate as possible” and discusses the importance of understanding that PSA levels can vary over time for all sorts of possible reasons. It is therefore rarely a good idea to make critical decisions on the basis of a single PSA test.

Indeed, Prostate Cancer International and The “New” Prostate Cancer InfoLink advises men who need to get their PSA levels checked on a regular basis to get all their tests done (if possible) at the same laboratory, using the same type of PSA test, and to have blood drawn for those PSA tests at the same time of day (probably best first thing in the morning on an empty stomach, which is what is requested when one has blood drawn for things like cholesterol levels too).

The second, by Tim Barley, is derived from interviews with a whole bunch of prostate cancer survivors and is headed “Prostate cancer survivors: what advice would you give the newly diagnosed?” This article is also full of good, straightforward guidance and advice, with the basic messages being “Don’t freak out” and “Do your homework”.

This correlates well with the core article on this web site entitled, “For newly diagnosed patients …” with its four-step set of suggestions.

4 Responses

  1. Thanks for posting the article by Worthington about PSA variability. It is one of the best I have read about the factors contributing to fluctuations in PSA test results.

    However, apart from the wise advice of not to take action based on a single result she does not offer any practical advice for men who are confronted with the task of determining how to identify meaningful “signals” in “noisy” PSA data sets.

    When framed from this perspective, the question shifts from determining which test result is the most accurate to one which can be answered very clearly: How can I determine if my most recent PSA test result represents the result of random variation about a stable mean or if it is a signal that some change has occurred in the system that has produced this set of test results?

    The principles of statistical process control (SPC) have been used for over 100 years to answer this question in manufacturing and healthcare settings and apply to PSA test results as well.

    I would be glad to share a paper my mentor (Dr. Donald Wheeler) and I recently completed that illustrates how to apply these principles in the context of an ongoing clinical trial to treat my prostate cancer.

    My email address is

  2. Your sitemaster has previously discussed the use of this methodology in assessment of PSA variation with Al Pfadt. The use of SPC in this context is certainly interesting. What we do not know yet, however, (at least in your sitemaster’s opinion) is whether the use of SPC can accurately evaluate the meaningfulness of PSA variation in individual patients. We are going to need to see data from some sort of prospective clinical study before we can be sure about this.

    On the other hand, if SPC can be shown to produce accurate projections in men with variation in their PSA levels over time, thsi would probably have the greatest value in monitoring men on active surveillance and in the follow-up of men treated with first-line radiation therapy (whos PSA levels can “bounce” up and down for all sorts of reasons).

  3. Thanks for your feedback, Mike.

    As Don Wheeler and I illustrate in our article, the principles of SPC are very well suited to analyzing the fluctuations in OSA test results, particularly when it is coupled with the use of semi- log graphing techniques developed by Ogden Lindsey. Any process that produces outputs can be analyzed by using these graphic technologies.

    Prostate cancer is a naturally occurring process that operates according to principles we are starting to understand more clearly. PSA production has been shown by many investigators to be tightly enough coupled to the growth and proliferation of an individual patient’s tumor that it can serve as a surrogate measure of it’s activity. The absolute value of any PSA test result contains little useful information. However, changes in the rate of change in PSA production are commonly accepted as providing actionable information that can be used to guide clinical decision making, as Don and I illustrate in our paper.

    I would be delighted to get your feedback on the results we present.

    Would you mind if I sent you a copy of the paper to review?

  4. For those who are interested, the principles of application of statistical process control to clinical data date back to this article by Pfadt et al., published back in 1992.

    The question of whether thsi concept can be applied to the evaluation of individual patients who observe variation in the PSA data remains unproven, however (as far as I am aware).

    Those who are interested are — I am sure — welcome to contact Al Pfadt for a copy of his more recent article, which I believe to be unpublished.

    What we are going to need to prove Dr. Pfadt’s hypothesis is data from a significant cohort of patients with variable PSA levels either while on active surveillance or (probably ideally) after first-line treatment with radiation therapy for localized prostate cancer. Until we have such data, the use of SPC as described by Al and his colleagues will remain an interesting hypothesis.

    This is in no way a criticism of Al’s hypothesis. It is simply a necessity of the way that science and medicine make progress. A hypothesis on its own is not enough.

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