It’s time for transparency in Canadian clinical trial data

Science-based health professionals hold the scientific method is pretty high regard. We advocate for evaluations of treatments, and treatment decisions, based on the best research. We compile evidence based on fair tests that minimize the risks of bias. And we consider this evidence in the context of the plausibility of the treatment. The fact is, it’s actually not that hard to get a positive result in a trial, especially when it’s sloppily done or biased. And even when a trial is well done, there remains the risk of error simply due to chance alone. So to sort out true treatment effects, from fake effects, two key steps are helpful in reviewing the evidence.

1. Take prior probability into account when assessing data. While a detailed explanation of Bayes Theorem could take several posts, consider prior probability this way: Any test has flaws and limitations. Tests give probabilities based on the test method itself, not on what is being tested. Consequently, in order to evaluate the probability of “x” given a test result, we must incorporate the pre-test probability of “x”. Bayesian analysis uses any existing data, plus the data collected in the test, to give a prediction that factors in prior probabilities. It’s part of the reason why most published research findings are false.

2. Use systematic reviews to evaluate all the evidence. The best way to answer a specific clinical question is to collect all the potentially relevant information in a structured way, consider its quality, analyze it according to predetermined criteria, and then draw conclusions. A systematic review reduces the risk of cherry picking and author bias, compared to non-systematic data-collection or general literature reviews of evidence. A well-conducted systematic review will give us an answer based on the totality of evidence available, and is the best possible answer for a given question.

In order for our evaluation to factor in prior probability, and to be systematic, we need all the evidence. Unfortunately, that’s not always possible if evidence remains unpublished or is otherwise inaccessible. There is good evidence to show that negative studies are less likely to be published than positive studies. Sometimes called the “file drawer” effect, it’s not solely the fault of investigators, as journals seeking positive results may decline to publish negative studies. But unless these studies are found, systematic reviews are more likely to miss negative data, which means there’s the risk of bias in favor of an intervention. How bad is the problem? We really have no complete way to know, for any particular clinical question, just how much is missing or buried. This is a problem that has confounded researchers and authors of systematic reviews for decades. Continue reading