This is the fourteenth 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 continue yesterday's comments on the observations of Dr. Ioannidis [1,2].
Dr. Ioannidis has published a list of conditions that reduce the likelihood that a research finding is valid . Here is his list, with some small modifications.
-1. Small studies are less likely to produce true research findings than large studies.
-2. Small effects are less likely to be true than larger effects.
-3. Research findings are more likely to be true in confirmatory studies (such as phase III trials that confirm observations made in a phase II trial) than in hypothesis-generating studies.
-4. The greater the flexibility in design, definition, and measured outcome, the less likely that the research findings will be true.
-5. The greater the financial and other interests in a study, the less likely that the results will be true.
-6. The hotter the scientific field, the less likely that the results will be true.
What does all this mean? In the cancer field, should we not trust clinical trials? And if we can't trust clinical trials, how can we make any progress?
My opinion is that clinical trials are very important, but their conclusions should be considered tentative, not final.
Prospective randomized clinical trials are types of experiments and have all the vulnerabilities inherent in clinical studies. They can be poorly designed, misinterpreted, invalid for under-represented patient subpopulations, un-repeatable, and falsified. The best validation of predictive tests will come from continuous clinical correlations with patient outcomes in medical centers wherein many different types of patients (male, female, different nationalities, different ages, concurrent diseases, multiple medications) will be managed. In particular, survival data need to be validated by post-trial epidemiologic data. The data that are being interpreted must be made available to the public, and the conclusions should be scrutinized and debated.
1. [Ioannidis JP. Why most published research findings are false. PLoS Med 2:e124, 2005.]
2. [Ioannidis JP. Some main problems eroding the credibility and relevance of randomized trials. Bull NYU Hosp Jt Dis 66:135-139, 2008.]
-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