Whether you're practicing clinical medicine, or working on a research project, all you're ever trying to look for are associations. In medicine, this could be an association between a clinical symptom, like a cough, or a potential cause, like smoking, with a diagnosis, say heart failure or lung cancer. There are two basic approaches for assessing whether an exposure is associated with a particular outcome: using experimental or observational studies. However, the strength of an association is judged by the robustness of the evidence. You've already learnt about observational study designs where, as the name suggests, you simply observe the study sample. A major problem with observational studies is that the observed groups may differ in many other characteristics in addition to the one being investigated. As a result, clinical medicine puts most emphasis on robust evidence from experimental studies or clinical trials, which are considered gold standards in terms of evidence. Today, I will introduce you to the best sort of trials, randomised controlled trials. Randomised controlled trials are experimental studies which compare and assess the effectiveness of two or more treatments, to see if one treatment is better than another. The treatment being tested could be a drug or some method of care, but there must always be a comparator group which acts as the control. Before a prescribed treatment can be used to treat any given condition, it should always be rigorously tested using clinical trials. Treatments being tested could be compared with no treatment, ideally using a placebo as the control. For example, if you were testing a new drug, the placebo would be a tablet which looked identical, ideally, to the active drug in every way, but does not contain any active ingredient. Trials using this method are referred to as placebo controlled trials. Alternatively, once you have a treatment that is effective and safe, you may test a new treatment against the existing standard treatment, to check if it is more effective or to examine what the side effects are, and how common they are. Information from the follow up of the control group allows the researchers to see whether the new treatment, or treatments, that they're testing are any more or less effective than the existing treatment or placebo. To maximise the value of the clinical trial, the choice of controls is clearly critical. There's no point in showing you a new drug or intervention is better than one that no one uses, or than the wrong dose of a drug that people do use. Randomised trials characterised by the fact that the study subjects are allocated by the investigator to the different study groups through the use of randomisation, and the investigators then intervene differentially on participants. It's an experiment. While randomised controlled trials are recognised as the gold standard study design for evaluating the impact of an intervention on an outcome, the process of randomisation alone does not wholly protect against bias. Incorrect analyses of the data can introduce bias, even where randomisation has been correctly implemented. It's important to preserve the advantages of randomisation during the conduct of the study, and in analysis. If you don't investigators may reach an incorrect and biased assessment of results. For example, by not evaluating patients according to the group which they were originally assigned. This concept of analysing patients according to which group they were originally assigned is called 'intention to treat'. Imagine you have 200 patients who had an acute myocardial infarction, heart attack. You randomised them so that 100 go to the coronary care units, and 100 go mountain climbing. In the coronary care unit, 18 died and 82 went home, so the survival rate is 82%. On the other hand, with the mountain climbers, 1 died because he was daft enough to go up the mountain, but 9 others who went up the mountain lived. The other 90 were lost, or if they were wise, they went home - we don't know whether they went home or died on the mountain. So indeed, they might have died at the mountain somewhere - you don't know. But if you just analyse the data for the 10 participants that you do have outcome information on, mountain climbing gives you a survival rate of 90% - one died out of the 10 you found. So, mountain climbing appears to be better than the coronary care unit. Now, nobody believes that. This silly story also emphasises that you have to try very hard not to lose patients. What happened to the 90 last mountaineers is critical to interpreting your trial, but equally importantly, you must include them in your analysis. If patients withdraw from the trial, you try to find out whether they are alive at the end, and what happened to them, and you include them in your original groups, because they were randomised to do that, even if they didn't take the drugs or carry out the instructions they were supposed to. Now you know the basic idea of why a trial should be randomised and controlled, and of the importance of selecting control interventions. Remember, importantly, you must account for missing trial participants, and include all participants in your analysis and in their original groups, regardless of whether or not they followed their allocation intervention.
