We have reported on the statistical methods used in the studies of pretreatment PSA dynamics as a marker for prostate cancer. Our key question concerned the value of adding information about a PSA dynamic to that of PSA alone. Consider the case of a clinician who needs to make a decision about a particular patient. The clinician will naturally have the patient's most recent laboratory report on hand, including the PSA level. If the clinician also wanted to use PSA dynamics to inform the clinical decision, he or she would have to look up prior PSA levels—perhaps contacting another clinic if the patient had recently moved—and then perform a calculation, which might be complex. In theory, this process could be computerized; however, doing so would still need to be well motivated. Moreover, some patients obtain PSA levels from different laboratories, complicating the clinician's task. To show that it is worth going to the time and trouble to use PSA dynamics, it would have to be demonstrated that doing so would improve clinical decision making. At a minimum, this would require that predictions based on PSA plus PSA dynamics are more accurate than those based on PSA alone; ideally, a decision analysis would also show that clinical outcome would be improved: for example, a demonstration that use of PSA dynamics would importantly reduce the rate of unnecessary biopsy without missing an excessive number of cancers.
We have found that, although PSA dynamics are associated with many end points, there is a near complete lack of evidence that pretreatment PSA dynamics are of clinical value for early-stage prostate cancer. Only two studies compared the accuracy of a statistical model incorporating both PSA and a PSA dynamic with the accuracy of a model that included PSA without the PSA dynamic: one showed no improvement in accuracy associated with PSA velocity; the other showed some minor improvements but was subject to verification basis. Studies comparing the accuracy of PSA with PSA dynamics similarly failed to show clear evidence in favor of PSA dynamics. We therefore conclude that calls to use PSA dynamics in clinical practice are not supported by current clinical evidence. Such calls would include both direct clinical recommendations, such as recommending biopsy for men with low PSA but a PSA velocity greater than 0.35 ng/mL/yr,10
and inclusion of PSA dynamics as an inclusion criterion for a clinical trial. This is on the grounds that, were such a trial to be successful, whatever PSA dynamic cut point was used would determine which patients should receive the study agent in clinical practice.
Comparing the accuracy of statistical models with and without PSA dynamics is not a complex statistical procedure. It therefore came somewhat as a surprise to us that only two studies did so. Moreover, only a third of articles included any evaluation of accuracy at all, with the majority of articles focusing purely on P
values and hypothesis testing. The time-honored distinction between clinical and statistical significance is of particular relevance here: it is perfectly possible for a marker to be a statistically significant predictor of an end point, but to add little clinical information (indeed, this is what we saw in one of our own studies11
Accordingly, we make the following recommendations for future research on PSA dynamics. First, efforts should be made to avoid verification bias; for example, researchers should avoid defining men not undergoing biopsy as cancer free. Methods to correct for verification bias have been published,14
but these have poor properties if there are few false negatives, and this is exactly what tends to happen in screening studies: men with low PSA velocities will generally not reach a high enough PSA level to undergo biopsy and so will not be found to have cancer during the course of the study. Second, the end point and the marker should be independent. As an example, in some active surveillance studies, a high PSA level defines progression. A high PSA velocity will inevitably lead to a high PSA; this inevitably creates a statistical relationship between PSA velocity and progression. Third, both PSA and PSA dynamics are continuous variables, and risk is unlikely to be homogenous on either side of any particular threshold. As such, both should be entered into analysis as continuous variables. Fourth, it is quite possible that PSA dynamics have a nonlinear relationship with outcome. It might be, for instance, that the risk of prostate cancer is low if PSA velocity is negative, suggesting no tumor growth, but also if PSA velocity is high, suggesting prostatitis. Accordingly, researchers should consider modeling PSA and PSA dynamics using nonlinear terms, such as splines or polynomials. Finally, and perhaps most importantly, researchers should assess whether PSA dynamics adds to existing clinical information. The most straightforward approach is to calculate predictive accuracy for a model that includes both PSA and PSA dynamics and compare this with accuracy of a model that includes PSA but not PSA dynamics. In some cases, such as predicting recurrence after surgery, it would be appropriate to include other predictors, such as stage or grade. Researchers should also consider decision analytic techniques to determine whether using PSA dynamics to influence clinical decision making improves patient outcome.
Absence of evidence is not evidence of absence,27
and we would not want to be interpreted as claiming that pretreatment PSA dynamics are not of value as prostate cancer markers. On the contrary, it seems highly possible that PSA dynamics might help predict at least one of the end points we study here. An obvious example would be a man undergoing screening with 6 monthly PSA levels of 1.2, 1.5, 1.6, and then 17 ng/mL/yr: the PSA velocity of 15.4 ng/mL/yr would be indicative of prostatitis rather than prostate cancer. Moreover, we see the value of post-treatment PSA dynamics, such as the PSA velocity at the time of recurrence, as being of proven value.
In summary, we have found little evidence that pretreatment PSA velocity or PSA doubling time are of value for early-stage prostate cancer. There is therefore no justification for the use of PSA dynamics in the clinical setting or as an inclusion criterion for clinical trials in this population.