[MUSIC] Hello, this is Janet Holbrook again. And today we're going to be covering the concept of randomization, which is one of the real core features of randomized clinical trials and probably the most familiar characteristic to most people when we talk about randomized clinical trials. For the first section, I will be going over the rationale for randomization. As I just alluded to, randomization is a key feature of the experimental design that a clinical trial is based on. So there are other features that are indeed as important as randomization and they include things like standardized treatment and having a prospective plan for data collection, adverse event reporting, as well as the regulatory requirements that go along with clinical trials. And these features tend to distinguish clinical trials from other types of observational studies and even from non randomized studies in some cases. So, it's important to recognize that the concept of randomization, or the drawing of lots, has been with us throughout history and it even is referred to in The Bible. It, it is the idea of drawing a lot as a way to ensure that the benefits and risk of an activity or event are equally shared or fairly shared across a group of people is a very early concept. One that's well illustrated with the concept of a military draft. Where the risk of war is shared across the population by drafting men mostly, into military service by the mechanism of a lottery. So this concept, which has been used in many areas, including how to divide some scarce resource up, or the concept of drawing the short straw. We first see the idea of randomization to be used in a medical setting in the 17th century. Van Helmont proposed that people be randomly divided or divided by lots to determine their treatment because he wanted to evaluate his form of care against more standard forms of care of the day, which included bloodletting and other potentially harmful interventions. And so he proposed that a number of patients be divided, and that at the end the half that are treated by him, according to his procedures, be compared to the other half of the patients that were treated with other procedures. And then there'd be, there'll be a count of how many people had died in order to evaluate bloodletting in healthcare. So just by way of background, many times we talk about or use the word random in, in our usual speech, or in ordinary speech and such as, it's a common saying, random acts of kindness, or random acts of nature, random acts of God. You fill in who the random acts are of. And what we mean by that is something that's not predictable and seems to be haphazard and there's no causal relationship that can be identified between things. I sometimes think my neighbors must think that I randomly mow my lawn. It's very haphazard, my pattern. But in the context of a clinical trial, we have a much more rigorous definition of random. And what it means is that the process, a random process is one in which there is associated with every legitimate outcome a probability. So there's a legitimate probability associated with whether you'll be assigned to treatment A or treatment B. Such as with every coin toss, we assume if it's a fair coin there's a 50/50 chance of heads or tails. With dice throws there's a one in six chance of any particular side of the die coming up. And this is a much more rigorous definition than the lay definition. The idea of randomization was first introduced formally into experimental design by Ronald Fisher. Many of you are probably familiar with his famous book, The Design of Experiments. And the question that Fisher was trying to address was to how to allocate plots of soil in agricultural settings to different treatments. There was no question about whether there needed to be controls on the comparison of the different treatments on plots. It was just a matter of, of how the plots were to be allocated because you can imagine that even within a field there's differences in sunlight and drainage. And they wanted to have a method of fairly distributing those effects among the treatments. And so what he proposed was to use random allocation, in the allocation of lots. Because he said by doing this it was the most efficient and unbiased way to estimate the measurement error, error associated with a particular treatment. And he proposed that any other way of doing this would either over estimate or under estimate the kind of random error associated with how the yield was in a particular plot. He also noted that there was some safeguard against non-normality in the outcome data, which was an added benefit of using randomization. So note that his arguments about the use of randomization are all kind of based on its statistical properties for estimating measurement error, and that it's the most efficient way to do an experiment. And, there's not a lot of emphasis in his work about the use of controls. The person who really is responsible for introducing randomization to medical science, is Sir Austin Bradford Hill. He was from an epidemiological background and his concern was about confounding that was associated with the type of people that got a particular treatment. Because he noted that people actually were quite variable in how they may react or respond to a treatment. And there can be in some ways predicted, and some of it's unpredictable, was a problem in estimating treatment effects. And because of the idea of a selection bias associated with treatment assignment, that the prognosis for the disease would be somehow be related to what treatment a patient was given. Commonly referred to as confounding by indication. So that people are given treatments based on their own unique characteristics. And what Bradford Hill was suggesting, is to get rid of that problem of the selection bias, in which people get which treatments, that random allocation be used. So the critics of a clinical trial would be unable to say that the groups were not equal at the start, that they were somehow biased based on the physician and caretaker's opinion about what treatment the patient should get. Perhaps, you can think of it in some senses that they might give the sickest patients the new treatment because they hadn't responded to older treatments and were in more dire need of a new option. And someone who was promoting a treatment had a interest in the treatment might do exactly the opposite and want the new treatment to look good. And so only give it to the best candidates. So Bradford Hill was very concerned about these type of biases and he proposed that randomization be introduced to clinical trials to eliminate this. And indeed, he did design and was involved in the first properly randomized trial that is documented well in the literature. And led the way for randomization to be used in other clinical trials. And this was a trial of streptomycin for tuberculosis in 1946. Interestingly, the use of randomization in this trial was based on that it was a fair distribution of risk and benefits to patients in terms of getting the new treatment. But it was also because this was in the UK right after World War II. There were very limited supplies of streptomycin. Mounting a randomized clinical trial was also argued to be the only way to fairly allocate the limited resources to, to patients. Which patients with TB were going to get this new medicine that had quite a bit of evidence for its efficacy. So as I've talked about, the rationale for randomization is to avoid selection bias and to avoid confounding by indication. That is, that there's some prognostic factors that would be related to treatment assignment, and thereby if they aren't somehow controlled, would bias our assessment of treatment. And it's important to realize that these prognostic factors, such as maybe age or severity of disease or even clinical centers, can influence outcomes as strongly or more strongly as many treatments. Remember that clinical trials are really designed to find small to moderate treatment effects. If there's very strong treatment effects, many times we don't need a clinical trial. So these prognostic factors can be quite influential, and so we use randomization to ensure their balance across the treatment groups. And that brings me to the next point, that randomization tends to produce comparable treatment groups on know or unknown confounders. But it's important to note, it's not a guarantee. It's still a probabilistic process, and there are times when you don't get perfect balance. That you may end up with what appears to be an important imbalance in treatment groups. For example, you could end up with many more women in one group than the other. And going back to R A Fisher, the randomization also assures the validity of the statistical test that we use generally to analyze trials. They're based on the idea that the treatment assignment, the primary variable we're looking for and effect in was randomly assigned across the population. And perhaps a less important point but is also a nice feature of randomization is that it gives us a defined time-point for trial entry. We know that we are going to measure events and attribute them to the appropriate treatment group from randomization onward. And so it's a very nice time zero that has some meaning in terms of how the patient was treated as well. I also want to briefly address the issue of using unequal allocation ratios for randomization. Pretty much up to now I've been talking or referring to randomization as sort of two groups one to one allocation. And that is certainly the most common design for parallel treatment trials. And one of the reasons the one to one allocation is so commonly used, it's the most powerful design. Other allocation ratios such as two to one or four to one are typically less powerful. And I gave you an example here of the power associated with different allocation ratios. But there may be reasons that you will compromise on power or alternatively accept maybe a larger sample size, because you want to use a unequal allocation ratio. It may be important to acquire as much experience as possible with the new treatment including the side effects and toxicity of that treatment. So you may want to have as many people as possible be exposed to the, the new treatment. That also may be an incentive for recruitment. If you're in the situation where patients don't have many options for how they'll be treated, it may be desirable to weight the randomization towards the, the new treatment as a tool for recruiting patients, so that they know that they have a greater chance of getting the new treatment. There may also be cost issues depending on how much the treatments cost. Very expensive treatments you, you may have a different allocation ratio in order to conserve resources. Also, you can imagine that in some cases that there may be difference in the expected variance of the outcome variable by treatment group. So you may expect that one treatment may have a more homogeneous effect than another treatment. And therefore you could argue for an unequal allocation ratio to optimize the power if there's a difference in the variance across the groups that's expected. I would say that that's probably an unusual reason for having an unequal allocation ratio. And finally, I, I want to speak to the definition of selection bias. Many of you who have taken other courses with the epidemiology department have heard people talk about selection bias as a problem in the study group, the entire group selected to be studied. That somehow the association between the intervention or the risk factor and the outcome is different in, in the group of people that is studied than in the target population. So the results from the study really aren't generalizable to the target population. So that's sort of an epidemiological definition of selection bias. However, in the trial definition of selection bias, we're really talking about eliminating the bias associated with the prognosis of the disease. So again to eliminate the confounding by indication. And we want to, to break that link between what the intervention actually is and the patient's characteristics. So it's more an issue of internal validity when we're talking about, in selection bias, in the context of clinical trials. So that wraps up this first section with trying to give you some of the underpinnings and rationale for randomization. In the next section, we're going to be talking about different types of randomization schemes.