Neoplasms: 8

This is the eighth blog in a series of blogs on neoplasia.

In the past few blogs, I've been trying to explain the disconnect between cancer survival data and cancer death rate data. The cancer survival data seems to indicate that we're making enormous improvements in cancer treatment. The cancer death rate indicates that Americans are dying from cancer at about the same rate as they had been a half-century ago.

Several days ago, I listed over a dozen biases in cancer survival data that contribute to an overly optimistic sense of medical progress.

In this and the next few blogs, I thought I'd review some of these biases. The purpose of this exercise is to explain that the interpretation of survival data is enormously complex and that survival data is probably not the best way to gauge progress in the field of cancer research.

Today, let's look at confounder bias.

Confounders are unanticipated or ignored factors that alter an outcome measurement. It is impossible to design clinical trials that account for confounder influences because most confounders are unanticipated. Confounders are the statistical byproducts of the "Law of unanticipated consequences," which simply asserts that there will always be unanticipated consequences.

Statins are a widely used drugs that reduce the blood levels of cholesterol and various other blood lipids. To the best of my knowledge, nobody expected that the use of statins would have any effect on the incidence or mortality of cancer.

In a recent study involving nearly a half-million male patients conducted between 1998 and 2004, statin use exceeding six months was linked to a significant lung cancer risk reduction of 55%. Participants who took a statin longer than four years had a 77% reduction in lung cancer risk [Khurana]. Let us imagine that the report is accurate and that we can eliminate deaths from lung cancer by 77% just by prescribing statins to everyone at risk for lung cancer. In the United States, about 90,000 men and 70,000 women will die of lung cancer this year [Jemal]. If this were reduced by 77%, we would prevent the cancer deaths of about 123,000 people.

Given these surprising findings, who knows what the effects of statins may be on a cancer treatment trial? If there were unanticipated influences of statin use among some clinical trial participants, who take statins for reasons unrelated to the trial design, then this would be just one example of a confounder bias.

Khurana V, Bejjanki HR, Caldito G, Owens MW. Statins reduce the risk of lung cancer in humans: a large case-control study of US veterans. Chest 131:1282-1288, 2007.]

Jemal A, Murray T, Ward E, et al. Cancer statistics, 2005. CA Cancer J Clin 55:10-30, 2005.

-Copyright (C) 2008 Jules J. Berman

key words: cancer, tumor, tumour, carcinogen, neoplasia, neoplastic development, classification, biomedical informatics, tumor development, precancer, benign tumor, ontology, classification, developmental lineage classification and taxonomy of neoplasms