My wife used to call me a science geek and it’s true. In high school and college, I fell in love with science as a way to explain the natural world. Since being thrown headfirst into the world of rheumatoid arthritis, it’s natural for me to apply this “geekiness” to all aspects of the disease both globally and personally.
A brief primer in research methods is necessary in order to understand the issues involved. Professor Trochim at Cornell University has an excellent website on research (http://www.socialresearchmethods.net/kb/index.php). He points out that research is based on some basic principles of logic and philosophy. First, it is empirical meaning that it is data-based. For example, if a pharmaceutical company is testing a new drug for efficacy in a clinical trial, they will administer the drug to patients and collect information (data) on the impact of the drug on symptoms. For RA, this may include the number of tender and swollen joints, bone erosion as viewed by x-rays or MRIs, pain level on a scale of 1-10, morning stiffness in minutes, etc.
Second, research is based on probabilities meaning, “the chance that a particular event (or set of events) will occur expressed on a linear scale from 0 (impossibility) to 1 (certainty), also expressed as a percentage between 0 and 100%.”[i] In order to increase the probability, large sample sizes must be used and careful measurements must be made. Yet, scientists can never be 100% certain about the data and observations they collect. Using the drug example again, results from a clinical trial can be used to argue that the drug helps some people but the scientists can never be 100% certain that it’s the drug helping the patients or some other factor.
Thirdly, science attempts to find cause and effect relationships – one factor causes an impact on another. This is usually framed in the form of a research question. For example, Does a drug reduce symptoms of a disease? As Professor Trochim says, “Experimental designs are often touted as the most rigorous of all research designs or, as the gold standard against which all other designs are judged.”[ii]
Experimental designs are required for drug clinical trials. Such trials usually include large numbers of patients in order to increase probabilities. Studies include a control group which includes a group of patients who do not receive the medicine being studied – a placebo. In a RA related study, the placebo group may receive a sugar pill or a saline solution. They are also usually double blind meaning that the patient and the scientists do not know what treatment the patients are receiving in order to minimize bias.
Let’s take an example from a popular biological treatment for RA – Enbrel. The drug company, Amgen, ran multiple clinical trials with many RA patients and collected data on multiple symptoms. They report, “A higher percentage of patients treated with Enbrel and Enbrel in combination with MTX achieved ACR 20, ACR 50, and ACR 70 responses and Major Clinical Responses than in the comparison groups.”[iii] A graph is used to present some of the results (see figure).[iv] Clearly those patients who received Enbrel had fewer symptoms than the placebo group and this was the basis for the approval of this drug by the U.S. Food and Drug Administration (FDA). However, some caution must be used when interpreting the results. Not all patients who received Enbrel responded to the treatment. Those who did respond still had symptoms of RA but to a lesser extent. These results were only for six months and it says nothing about the long term impact of the drug. Side effects, either short term or long term, are not reported by this graph (side effects are reported separately).
Understanding the processes of research helps patients understand the benefits and limitations of treatments. This also leads to personal application. I once had a doctor tell me that he only wants to change one medication at a time in order to determine cause and effect. That’s a smart move in order to find out what each drug is doing. In some ways, each patient is a mini experiment trying to find out what treatments impact symptoms. But the limitation is that it is a sample size of one…hardly enough to make generalizations to other patients since everyone responds differently. I also read various patient experiences on website discussion boards and drug review sites. While such individual reactions may be interesting and worthwhile for gaining perspective, they must be interpreted with caution as they may not based on sound scientific methods and sample sizes. Patients must understand the limitations of such discussions. Just because a medication didn’t work for me or had extreme side effects does not mean it won’t work for someone else. Sometimes, too much negative information ends up on such sites driving patients to fear resulting in refusal to take medications.
Patients must understand the processes of research and be good consumers of the plethora of information that comes our way.