So, coming back to this study of the pain measurement and the, the sample use case of around morphine and Marinol. Now that we've defined the study, and we've sort of laid out how the procedures will work and what the What, what the population will look like, and how we'll kind of lay them out on the, on a calendar from a, from a study by it. Sorry. Participant, by participant basis. And what we'll be doing there? I think now, the next thing we want to do is look at the, look at brainstorming data collection. You know, let's again go back and see, see what we would collect, how we would, we would collect it. After that, we'll go into actually implementing this in the electronic data capture. But before we start thinking about electronic case report forms it's always very, very wise to step back and think about, you know, all of the different pieces of what you're trying to measure, what you're trying to study. So, so that at the end of the day You've got something that, that that, that regardless of how the, the data come out you, you can either prove or disprove the hypothesis and you've got some scientific benefit that can be published and, and there again benefit society. So, as we go through this, we'll, we'll, we'll kind of go back to a few of the, the important concepts, or the best practices that we've talked about early, earlier. [COUGH] One of the, one of the very, most important of those concepts is again, sort of take a step backwards. Think about things before we start doing them. So, it's very important that we, that we take the time right now as we're considering this study. rather than hurry it along because if we hurry it along we'll, we'll undoubtedly forget something that, that may in fact be the difference between having a, a good scientific conclusion and, and maybe having something that's inconclusive. Again taking that concept one step further. It's an ethics thing. So we are no matter how safe we define a study, no matter how much societal benefit there is to that study, and, and that's what we're, that's our end objectives in any research study that we are conducting, there is always going to be some risk. Or some inconvenience to the individuals that are working with us on studies. And so I always teach in data management that, that doing this, planning things up front it's one of the most important things that we do. If we don't, and we don't get as much scientific benefit out of that even potential risk, or discomfort, or inconvenience to our subjects we're really doing a disservice to everyone. So, another good practice that we'll think about as we're going through as being exact and discrete when defining our variables. We know that at the end of this study, we want to be able to analyze this data, and we want to be able to sort of come up with an answer. Of whether there is a synergistic effect between morphine and marinol. if we don't think hard about it, if we don't come up with things that are quantifiable in, in a way that is structured and measurable, then you know all we'll have at the end of this study is a hunch. Or, or, or something that we're not quite sure of [COUGH] We will, we'll look, especially at the beginning, of how we'll kind of start brainstorming this. We'll look at the confounding factors. You know, making sure that at the end of the day, when we get ready to study this, and if, if we see variations, that we've measured. Things that, up front may be confounding factors that, that could potentially, play a role in, separating our responses, even among healthy volunteers. So, so first let's think about that. So, demographics you know, I, I've worked many, many studies over many, many years and whether you call these demographics or participant data, or, or, you know, somewhere in between. There's always sort of this collection of one time data about an individual that, that you're studying. So, so if we look at the, you know, maybe, maybe taking the first one of those might be name. You know we need to identify that patient for later. And, and again, here we could digress a little bit and we could talk about the fact that some studies are, are completely deidentified in terms of the data management. They, they don't know and they don't need to keep track of who, who an individual is after a visit. So maybe it's completely deidentified. in this case, we'll just make an assumption that we do need identifiers. And so, the first thing that we might we might consider is a name. So, you know, name is pretty easy. Mine is Paul Harris. We could, we could have a, a, if we were doing this on paper, we could have a put your name here line or if we were doing it electronically, we could put your name. You know, in this box, and I might type Harris comma Paul, and I might type Paul Harris. I might type P. Harris. So, so to get rid of the ambiguity there, it's probably a good idea, even thing about things like the discreetness factor when we, when we get into names. And so we'll collect that as a first name and a last name. information about our participants like their, their sex. So, so here you know, we, we know that potentially by going back to the literature we know that there might be differences in how males and females characterize and, and deal with pain. And so given that that may be a confounding factor in the future, it's probably a good idea to collect that data. And so we, we, learned in one of our previous lectures about best practices that if we're going to collect data, it's a good idea to, to, in, in a categorical way like this, it's a good idea to code them. that way we don't have the situation where an individual types in male Or M and female or F. You know we have everything structured. We'll sort of list in one place that, that male equals one, female equals two, and maybe transgender equals three. That way we, we've got a structured way of sort of collecting it. But also it's going to help us a lot downstream when we get ready to analyze the data, to have these in structured format. Hair color, so there's, there's recent studies that show that at least individuals with red hair may may process and deal with pain in a different way than individuals that do not have red hair. So, it's a confounding factor, you know, maybe when we get ready to analyze the data We see some grouping in the answers, and if we, if we go back and we stratify by hair color, maybe that would explain some of the variation. If that's possibly the case, then we'd better collect the data. And so maybe we would again go back and code black hair as one, dark brown as two, light brown as three, blonde as four, and red as five. And I didn't make these up. I went to the, to the, to the literature, saw how other people had collected these types of data after I looked at how you know, hair color could possibly be a confounding factor. And by the way, this is one of those examples where I'm not exactly sure, but the study in question, this morphine study. Was done at least around ten years ago. And so this is an area where, you know? It could very well be that this hair color finding happened after the fact. And so, it just sort of drives home the point that thinking about things. And thinking about things that, that, that actually might play, play a role. I-, it's important before we start the study. even considering the fact that there might be things that are unknown now. That, that might become known later. And we talked about that in a previous lecture maybe about collecting DNA for the same types of reasons. Handedness, so just, just doing a quick review of the literature shows that at least in some cases, in some circumstances, people that are left-handed might process and deal with pain different than people that are right handed. So, not a bad idea to collect, in part of our demographics about each person, whether they're left handed or right handed. And again, we'll code those, one is left, two is right. body mass or you know, obesity, there's some recent literature that, that talk about the fact, talks to the point that You know individuals at, high, high obesity levels might have, might process pain in a different way than individuals that, that, that are leaner. So here we get into one of those questions, well how are we going to measure that one? Should measure, Just measure weight, or should we measure BMI. I think it's a good idea to, to measure BMI which is one of those characteristic measurements of, of sort of obesity, and, and, body mass. But, but, lets not collect it and store it as BMI, we talked in a, in a previous lecture about the fact that we won't collapse our variables until we need to. So why not just collect it, since BMI is made up of weight and height? We'll just collect those as two different measurements. we'll collect weight, and we'll collect height, and we can always, we can always calculate the BMI from those measurements. This sort of brings up a question of, well, when do we collect those data? in a non-pediatric study where we're dealing with adults, and for a fairly short amount of time, you know, one, two years. This study is a really small one. We'll probably have a patient enrolled and finished within a month of, of, of, of engaging in a relationship with that particular volunteer. But, in general, you know? In, in cases like that one. Probably, the individual's height is not going to change during that study. If it's pediatric study, maybe so. in, in which case, we'd want to measure height at every visit. The weight, you know, for a month study. It's probably not going to fluctuate a great deal. But, you know, it wouldn't hurt, if, if, if it's convenient, or if it's possible. It might be that we'll collect the height, on the demographics, on the screening visit. We'll calculate, we'd, we'd collect the weight there as well. But as we'll go along in the study, even over a month time period, maybe we'll collect the weight each time they come in for a visit, because we do, we all Fluctuate in weight. And so, so getting that at the visit level rather than at the one-time study entry level might make sense. Other factors that we might consider. smoking status. You' know there's some information in the literature that talks about smoking being a confounding factor in Individual sensitivity to pain, anxiety and depression, how athletic an individual is, stress level on the day of the study, you know other things again we'll definitely want to collect in that demographics or screening visit would be whether they're pregnant or not, whether they're a current drug user. And again we're going to collect those and we're going to use those as inclusion, exclusion criteria so that you know, if an individual says that they are a current drug user or if our testing indicates that they are currently using drugs. Then we would, we would remove them from the study, but we would still, more than likely, collect the data about them and maintain the data about them so that we would know that, at the end of the study, that if we had people that didn't go forward that we knew why. And that we could convince ourselves that we weren't biased in treating in any particular patient or patient cohort. Sorry, volunteer cohort. Okay age. Age is a thing that we might consider. There is information in the literature that says people in different age brackets process pain differently. So here, again, it's kind of like that BMI one. Do we, do we collect the data do we collect age and store, you know, how old you are? What if you, what if your birthday is tomorrow? So I'm 49 and, and tomorrow I'm going to be 50 or I'm 70 and tomorrow I'm going to be 71. It, it's a good idea because again we can calculate age. If we know the date of your visit and we know the date of birth. And we can always calculate the age and you know, it's, it's just a good idea to sort of think this in and make the, make the measurements as, as discrete as, as we can. So, what I would recommend there would be we collect the date of birth on the initial visit and then each particular visit would also going to be storing that the date of that visits, so that we can calculate the age across the entire study. Ethnicity, so, so here's where you, you know, we get into collecting information on ethnicity and race, which again might very well be confounding factors, in, in this particular study. Here's where you know, we sort of have to look at local context. So this study was conducted in the United States. in the United States we have some fairly structured and, standardized ways that we collect data around ethnicity as well as race. And those are part of information that our National Institutes of Health, put out and sort of have guidance they have around gathering and reporting this information. So for this particular study. We'll, we'll go with ethnicity as either Hispanic or Latino, or not Hispanic or Latino. And then the race will be cap, captured in these particular categories. Again, we al, we always have to consider local context. If we're doing a study in Puerto Rico. Or if we're doing a study in Japan or France. the-, these racial, these racial and ethnicity categories maybe they don't make sense for that particular population. So don't take any of this information as a this is exatly how you should do a study. But look at it as more of a guidance and way to think through things. Okay. So we're at the end of this video segment and we'll pick up again in the next one with collecting data at the visit level.
