Now that you focused down that question, you need to turn this into a hypothesis or a description. So, people often talk about hypothesis-driven research. But if you're just doing a description, like how many people in the hospital get disease X?
That is not a hypothesis. You are just describing something. And what that is, is actually usually hypothesis generating research.
So, you may want to use that information. So, I recently did a scientific review that wanted to look at comparing outcomes in patients before and after they had instituted a new treatment -- a new guideline for testing people for tuberculosis in their system. And they wanted to see what impact implementing that guideline actually had.
So, the guideline had only gone into effect literally a year ago. So, one of our questions was, do you even know how many people are going to have tested positive for TB in the last year? Because if it's two people, you're not going to have much to analyze in that timeframe.
So, they needed to do that descriptive piece first before they could go test their hypothesis. So, what is a hypothesis? So, a hypothesis is not a question.
It's a statement. It's a statement about what the investigators believe to be true about nature, and the relationships of two or more variables to each other. So, in other words, a hypothesis doesn't say, I am going to find out whether drug X is better than drug Y.
A hypothesis is drug X is better than drug Y. And then you formulate a question around that. A hypothesis almost always entails a comparison, but all research is not comparative, as I've said.
So, you need to differentiate qualitative from quantitative research and descriptive from analytical research. Descriptive research is, like, what I just described where you're going to describe the number of people in your institution who have tested positive for TB by a skin test. That's descriptive.
Analytical means you are comparing one group to another in some way and maybe to compare people who have the disease versus don't to look for risk factors of getting it, or it may be able to compare people who have got drug X to drug Y to see if drug X improves outcomes compared to drug Y. So, qualitative and quantitative research are really two distinct endeavors. And there is more and more information coming out or more realization about the impact of qualitative research.
Qualitative research is often used to generate the hypothesis that are then evaluated in a quantitative way and quantitative research. So, the aim of qualitative research is the complete, detailed description in words. So, for instance, when we developed the outcome symptom scale for influenza, the first step was actually to interview patients who were tested positive for influenza, to see what symptoms they had.
The output of that was words. It was the patient's descriptions of what they felt when they had influenza. The second step of that project was to quantify how many people had what symptoms.
That's a quantitative question. So, the qualitative research develops observations for further testing, whereas the quantitative one constructs statistical methods to explain the observations. So, in the qualitative research, you only know roughly in advance what you're looking for.
You are kind of groping around for what you're going to test quantitatively later. On the other hand, quantitative research is not a data dredging exercise. You want to clearly state in advance what you're looking for.
So, the qualitative part comes early in the phases of a research project whereas the quantitative part comes later. And the design of a qualitative study may emerge as the study unfolds. On the other hand, a quantitative study carefully lays out in advance all aspects of how the research is going to be designed and the data is going to be collected?
The actual instrument in a qualitative study is the researcher themselves. So, if you're doing patient interviews, what's the tool. It's the interviewer.
The interviewer is asking the person questions and the words are the output. On the other hand, in a quantitative research study, the researcher uses other tools like, say, the questionnaire that was developed in the qualitative part or equipment that you might have developed to a laboratory test to collect numerical data. So, the output here is numerical data rather than just descriptive words.
So, we already went through this descriptive research, provides an account and delineates the components of a problem. So, for instance, a case report. So, I was just on service in July and we saw someone who was neutropenic after receiving cancer therapy.
It happens all the time when people get cancer chemotherapy. But this person developed epiglottitis. I don't know if you folks know what that is.
Epiglottitis is what people think killed George Washington, but it's usually was a child disease. It's when the epiglottis that's in your throat swells up to the point where it closes of your windpipe, so you can't breathe. That hardly ever happens in people that are neutropenic, meaning their white blood cell count is low.
Because what do you need for your epiglottis to swell? You need white cells, so then it will cause the inflammation. So, the medical student that was on with us did an excellent job of actually looking back through the literature to find all the cases of epiglottitis that ever occurred in people who are neutropenic.
And we then put that together in a case series and describe, here's the kinds of people that get this disease. Very useful. So, if anybody else ever runs into the same problem we did, they can now look back through this and say, here's the kind of people that get this disease.
Does it say that what we did for the patient is the best thing to do? No. There -- that's a quantitative question that we couldn't answer by the use of case series.
That needs analytical research which is testing one or more hypothesis in a quantitative fashion. Distinction is not as clear as descriptive research often contains comparisons, but you can assess causality. So, I may want to say -- for instance, I'm going to -- you could look at the case series on epiglottitis and say, well, it appears there is more cases now that there used to be.
But that may be because people are more aware of it and are more likely to report it. It doesn't at all mean that it's actually more comment. I can't make that assessment.
So, the push for hypothesis-driven research tends to make description sound less valuable. But these descriptions are absolutely necessary in order for you to be able to form the hypothesis that you were going to test in the future. So, in other words, research is really a stepwise approach to got to develop the question before you can answer the question.
If you skip over those earlier stages or assume that it's already been done, your research may be on shaky ground to begin with. So, once you come up with that kind of question, then you have to match the kind of study to the kind of question that you actually want to answer. So, I was jokingly -- say -- I made this graphic once on an airplane, and I think that woman sitting next to me wanted to jump out of the window because putting all these things, little lines, on PowerPoint is not my forte.
But what I tried to do is to try to separate this out into the different types of research of descriptive versus analytical. Notice I don't have anything under descriptive. You don't need to do anything.
You just count up the number of people that have a certain disease and describe what you saw. On the other hand, analytical research entails some kind of comparison. So, there are several ways to make that comparison.
The first is to divide things up into experimental versus non-experimental research. So, non-experimental is often called observational research. So, the distinction between these in experimental research, it's the investigator who decides who gets what treatment or who gets what exposure.
So, randomized trials put -- assign the patients to drug X or drug Y or drug X or placebo. That is done by the investigator through the process of randomization. In an observational study, the investigator doesn't have a hand in who gets what.
They're just watching what happens. The clinicians and practice give what they think is best and then the investigator counts up the results. There are three types of observational studies.
One is called a cohort study, one's a case control, and then one is cross sectional. So, a cohort is one that moves forward in time. It starts with the exposure and then looks forward to the outcome.
So, you start with exposure and you go to outcome. You can do that retrospectively, meaning that the data is gathered before your hypothesis is made. So, a retrospective cohort study would be me going back and looking at the last -- which is essentially what we wanted to do with that study, looking at blood cultures in the clinical center.
I want to compare people that had a resistant bacteria in their blood compared to people that didn't have a resistant bacteria, and then see how many people died in each group. That's a retrospective cohort study because I'm starting off with the exposure. Who had the bacteria in their blood and the outcome is death?
But -- and I'm looking forward in time, even though the data has been collected previously which makes it a retrospective study. A case control study does the opposite. It actually starts with the outcome and then looks backwards in time.
So, these are always retrospective because you're always looking backwards. So, you -- this is actually how the first studies were done where they looked at lung cancer and smoking. So, they looked at people who had lung cancer and looked back to see that more people who were smokers had gotten lung cancer than the people that didn't have lung cancer.
And then finally, there's a cross sectional study which looked -- it doesn't have any time component. It just looks across the data at one particular point in time. Experimental studies can be divided into two types.
Those are the randomized and those that are not randomized. So, randomization is a process by which patients are assigned to give interventions randomly. So, you may generate a table of numbers or even use a coin that you can flip to decide which group people go into.
But it is not a systematic process of assigning folks to the intervention. Non-randomized trials don't do that. So, for instance, in a non-randomized trial, you may do what's called a historical control.
You can pair people who got the new treatment to people in the past who didn't get it. So, for instance, in the hospital, studies on handwashing are often done this way. So, what happens is a hospital implements a new protocol for handwashing, count up how many people get infections in the hospital and compare it to what happened in the past when they didn't do the handwashing protocol.
The problem with that is that many things can change over time between the time when you implemented the handwashing and the time that you didn't. And so, you can't really assign that the outcome is due to what you thought it was. The process of randomization allows you to assign causality to the outcomes.
So, if you randomize people to drug X or drug Y, everybody in the study, if randomization works, has an equal chance of dying from the disease. So, nobody is different in the study at the beginning of the study. And then you follow them forward to the outcome.
The outcomes then differ based on -- what's the only thing different between the groups? It's this group got the intervention and that group didn't. They were all equally likely to have a specific outcome at the beginning of the study.
So, what randomization does not do is account for biases that happened after the study begins. So, I never saw this movie, but there is a movie called "The Dictator," I guess, which is one of those Sacha Baron Cohen movies. So, I guess, in this movie, he's running in the Olympics in country.
He's the dictator of the country. So, he is running in the race, and he starts off and he shoots the gun off at the race. And he starts running, and he turns around and he shoots all the other people in the race.
So, did they all start at exactly the same point? Absolutely. That's randomization.
What happened after the study? He shoots everybody. That's missing data.
Right? So, that randomization does not account for stuff that happens of the race has been started. So, there are four different types of randomized trials.
One is when you compare an intervention to placebo and -- or you compare intervention to no specific treatment. The difference between those is that you can blind a placebo-controlled trial. Placebo means that you're giving people something that looks exactly like the thing that you're testing, so nobody knows what you're going to get.
On the other hand, if you compare treatment to no treatment, people know who got what because you can know who go no treatment, and you know who got the treatment. The other kinds of trials are those response trial where you compare higher doses of the intervention to lower doses of the intervention. And lastly, there is an active controlled trial where you compare drug X to drug Y which have their own special issues.
You can actually do those studies to evaluate whether the new intervention is better than the old one. But in what I do in infectious disease, many of these studies are not used to evaluate that the new intervention is better. They want to rule out that the intervention is worse by some amount.
And you'll hear more about that kind of specific trial design later on in the course syllabus. So, let's get down to developing hypotheses. The more specific you are, the better, because people can understand what you're trying to evaluate.
So, for instance, let's take the hypothesis antibiotics are effective in ear infections in kids. Well, that doesn't tell me what antibiotic, what kind of kids, or even what kind of ear infections. So, saying amoxicillin is effective in acute otitis media in children between the ages of 2 and 6.
Well, that's better because I'm describing the kids better. Amoxicillin is effective compared to placebo. Okay, that's even better.
Now, I know what I'm comparing it to. In reducing pain, okay, now I specified the outcome, in children ages 2 to 6 years with initial episodes of acute otitis media. Now, I described the disease.
That's the best way to describe it. The more specific you are about your research hypothesis, the more people can understand what you're looking at. So, that gets down to developing specific aims and objectives for your study.
So, once you've chosen an overall research question, that gives you the why. It's the rationale for doing the study. But then you need to answer a bunch of other questions.
Who are you actually going to study? That defines the population. Where are you going to do the study?
Is this going to be on hospitalized patients or outpatients or both? When are you going to do it? What's the timeframe in which you're going to run the study?
And is this study going to be prospective, meaning that the hypothesis comes first and now you're going to collect the data; or is it retrospective, meaning I come up with my hypothesis, but the data has already been collected in the past that I want to look at. What variables are you actually going to measure? What interventions are you going to look at, and what are the outcomes that you're actually interested in looking at?
Those things all fall into a rubric called content validity. And then how are you going to do this? What tools are you going to use to make the measurements?
Oftentimes, what happens is that we are sort of stuck with what we're stuck with. So, in other words, if you're doing a retrospective chart review and you want to look at how many people had disease X, you may look through the chart. And it says in the chart, this patient had pneumonia.
And you go to look for some confirmation of the person having pneumonia. You can't find it. It's not in the chart.
You can't find an x-ray. You can't the doctor thought person this person had pneumonia. So, you're stuck in retrospective data with what's already in the collected data.
On the other hand, a prospective study, you can define ahead of time what you actually want to measure. For both situations, though, planning is really key. And there is a thing in the efficiency literature that says, "Failing to plan is planning to fail.
" If you don't think through these things ahead of time, you're going to get stuck in a place you don't want to be because you're going to run into a problem you didn't anticipate. So, my favorite is when I review protocols, and you get sort of stuck into this vague language. Back in 1992 when Bill Clinton was having his issues with Monica Lewinsky, he uttered the famous, "Well, it depends what the definition of is, is.
" That's not the position you want to be in when you're designing clinical research. So, a protocol I reviewed recently said, clinical outcomes will be divided up into clinical success and failure. Well, I hope so.
But I don't know what that means. How is success defined? How is failure defined?
So, without being specific about those things, you're now more informed than you were before you read that sentence. So, that gets to this issue. This is one of favorite shows to watch on Saturday morning.
It's been on for, like, 35 years now, This old house. I'm very jealous of these guys because they have every power tool known to mankind, which my wife will never let me buy. So, I'm stuck doing it by hand when these guys always have the fancy tools to be able do it.
But they have the right tool for the job. And that's actually what you have to think about in clinical research is, you need to apply the right tools to what you're trying to do. So, what are -- before we move on to talking about efficiency in clinical trials, let's talk a little about what are some of the common pitfalls that people run into when they're trying to come up with the research question design or study.
One is they let feasibility issues become paramount. And it's better to change your question than it is to develop a research study that's invalid scientifically just because you don't have the resources or whatever you need to do -- answer that question properly. Another one is taking on too many questions and because you do that, you don't answer any.
You've got too much information stuck into a single study or becomes so difficult to do that you can't accomplish it. Lack of clarity on the hypothesis and the research question to begin with, or not choosing a study design that matches the question. So, one of the things I noticed in infectious diseases, what I do is we say, there is an unmet medical need for this drug because there's resistance to the old drug and people are dying.
And then you look at the study design and it's not designed to show the new drug is better than the old one that you were just told so bad. So, it doesn't match the research design to what the stated problem is to begin with. And then finally, the issue we talked about a vague specific games and variables and unclear measurement properties of the tools that you're actually using.
One of the things to be very warry of is the two words that you'll see in a lot of protocols called clinician judgment. My judgment is not like my colleague's judgment. It's not, like, the person next to his judgment or her judgment.
So, when you put that in there, you are inherently putting in some vagueness into the study. Sometimes, that's okay. Maybe, what you want to evaluate is clinician judgments.
Why the clinicians make these particular judgments?
