Week 5

This week, I've realized why (1) research is all trial and error and (2) small sample sizes are the bane of my existence. 

I finished up the graphs that I mentioned in my last post. I created individual scatter plots for my data, in which I listed number of visits (independent variable) on the x-axis and symptom severity (dependent variable) on the y-axis. Then, I generated a best fit line for each set of data to compare the study and control groups. Since speech therapy only addresses cognitive issues, I just designed graphs for each of the 8 cognitive symptoms, as opposed to all 23 physical, behavioral, and cognitive symptoms. Also, I created a separate graph for average cognitive symptom severity vs. number of visits. This provided a look at the overall improvement in patients for both the study and control groups. The only problem was that the control group (who received no therapy) appeared to improve more than the study group (who did receive some therapy). I took a glance at our raw data to see which study patients could be skewing the graphs, and it turns out one of the study patients only completed their first speech therapy visit. This visit is typically just another evaluation, so it really wouldn't have made much of a difference in symptom severity. Because I have such a small population, even one patient could affect everything. As a result, we decided to omit this patient, cutting down my sample size yet again. I know, I know. Reducing my 7-subject population even more sounds like the worst possible thing I could do... But it worked. After removing one patient from the control group-- just to even things out-- the graphs looked much better. 

Fig.1

 Fig. 2

Fig. 1 is before patient omission. Fig. 2 is after. (Note: The x-axis should actually be labeled "Number of Visits". Whoops.)

Recreating each graph over and over again helped me see how much patience is required in professional research (hint: it's a lot). I'm just glad that we managed to make our ridiculously small sample size work. Apparently this type of analysis is common with medical research because some extremely rare diseases only affect a tiny portion of the global population. It's nice to know I'm not the only one who's faced this challenge.

In addition to these graphical representations, I need a way to quantify the impact of speech therapy on DV TBI patients. I analyzed the slope and correlation coefficient for each data set to better understand this cognitive impact. The slope is pretty self-explanatory. Rise over run and all that jazz. The correlation coefficient, on the other hand, is a bit more complicated. This measurement, r, indicates the relationship between two values. It has a range between -1 and +1, where -1 shows a perfect negative correlation and +1 shows a perfect positive correlation. 0 signifies no correlation. In layman's terms, a negative correlation indicates that one variable increases as the other decreases (and vice versa). A positive correlation indicates that both variables will either increase or decrease. For our data, we hoped to see a negative correlation, in which symptom severity would decrease over time as patients continued to come in for speech therapy. We also hoped to see a significant difference between the study and control groups, demonstrating that speech therapy is actually worthwhile. The difference between the two is clearly visible in Fig. 2, but I need more concrete evidence than just "eyeballing" it. This is where I've reached the limits of my knowledge of statistics. Comparing the correlation coefficients for study and control involves a more extensive test, which I've never encountered. I contacted a local statistician to go over what I have. Hopefully, she will provide some suggestions for my data analysis methods. 

After finishing up at the clinic this week, I went with my mom to a meeting held by the National Association of Hispanic Nurses. Let me clarify: I don't plan on becoming a nurse. Still, I'm so glad that I went because some of the topics that were presented are absolutely applicable to the population that I'm working with. For example, one of the speakers discussed the term "non-compliant patient". In the eyes of most medical professionals, a non-compliant or non-adherent patient refuses to take their prescribed medication or follow through with their recommended therapy. Even I mentioned the word "refuse" in one of my earlier posts. Now I'm beginning to understand that there are other factors to be considered here. Lack of education, financial status, domestic problems, and other circumstances can prevent a patient from following their treatment regimen. On top of all of the physical and mental symptoms that arise from a TBI, DV patients have the added concern of their physical safety and/or the safety of their children. There's more to it than just a simple refusal. In fact, that's the very reason why doctors, social workers, and therapists are all necessary to treat these patients. Social and medical issues must be addressed together. 

Week 4

It's week 4 and I'm finally starting data collection.

Regardless of what my badge says, I don't have access to the clinic's database like all of the other employees because of my age. So, instead, Dr. Zieman and the clinic's program coordinator have to print off stacks (and I mean stacks) of patient information and white-out their names before giving them to me. I'm extremely grateful that they're willing to take the time to even do this. 

As far as the "screening" process goes, patients were chosen carefully by Dr. Zieman based on several criteria. Age was the biggest factor. We sorted through patient files to age-match each study group patient to another control group patient. This ensures that the mean age for both groups will not affect our results. Other factors such as history of substance abuse or history of mental disorder did not necessarily disqualify patients from the study, but they had to be considered. There are only 300 patients total that are treated here at the clinic, and only about 40 of those are reported domestic violence victims. Only 7 of the 40 DV patients have attended speech therapy more than once. 

This cuts down my target population to below 10, which is a problem.

It's a generally agreed upon fact that statistical validity is dependent on sample size. With such a small group (14 patients with both the study and control), I fear that my results will be inconclusive. Since I don't have the resources to expand my study, there's really no other option but to work with the population that I have. I'm hoping for the best. 

Actually compiling the data isn't too difficult. Essentially, I've been reading through each patient file and noting their age, sex, number of visits, and symptoms in an Excel spreadsheet. Their physical, behavioral, and cognitive symptoms were reported by the patients themselves using a severity scale (from 0 to 6). Each time the patients visited the clinic, they filled out this same symptom questionnaire. My spreadsheet records the symptoms on a 0 to 6 scale for each individual visit (for both the study and control groups). After completing this spreadsheet, I plan on graphing the data to compare the experimental and control groups.

Patient Symptom Severity Sheet

I haven't been living up to my role as clinical observer much this week because I've been busy collecting all of my data. However, I did get to shadow Dr. Zieman as she injected pain medication into her patient's skull. Apparently head injections can drive a grown man to tears. You learn something new every day. 

Week 3

I started off the week with an episode of nostalgia.
I was invited to the neurology department's research symposium, where all of the doctors gather to present their research projects from the last year. In other words, I attended a professional science fair. I was introduced to several other neurologists among rows and rows of poster-board displays, and all I could think of was the amateur melt-an-eggshell-with-soda project I had done as a nine-year-old. Of course, their experiments were much more complex, but it was still interesting to see the scientific method in practice. These posters contained a vast array of research topics ranging from dementia in patients with Down syndrome to ketogenic diets as a form of seizure treatment. It was especially exciting to know that I recognized some of the terminology (i.e. PCR, Western Blot Test) discussed in these studies from my biology classes.  

Dr. Zieman, my mentor, presented her own research from this last year, a study of domestic violence patients with TBI. Although different from my own, her research is the inspiration for my project, so there are some similarities between the two. First of all, her study was retrospective. There are several reasons why my own research should be conducted in the same way. The most immediate issue is the fact that I don't have access to my own patients. I only have access to medical records from former patients. Additionally, due to time constraints, I'm unable to search for a sample size of current patients who meet all of the criteria for my research.  The process of selecting patients and debriefing them about the study would be too lengthy. So, instead, I've decided to use information that is already in the system. 

I spent the rest of the week observing more patient visits. Despite the fact that I've already been a clinical observer for weeks, I haven't seen everything. I'm continually surprised by how different each case can be. One patient might show all of the classic symptoms of a mild TBI: headaches, nausea, dizziness, irritability, etc. Then the next patient (who also has a mild TBI) could show no common symptoms of a mild TBI, but instead display symptoms characteristic with Bell's palsy (muscle weakness in one half of the face). There's unpredictability everywhere. That's what makes my job so interesting.  

Week 2

Although most patient analysis is based on quantitative data, there is still an element of subjectivity in the medical field simply because there are humans involved. I've continued observing patients from various backgrounds; different ages, genders, ethnicities, education levels, beliefs, etc. Some patients come into the clinic with research articles and questions, eager to learn more about their condition. Others are just determined to get back to work. Perhaps the most problematic aspect of this is the fact that everyone has their own opinion. In some cases, patients are doubtful of their physician. While they may be severely misinformed, they still have the right to refuse treatment. This is an issue that I became interested in since I first began learning about domestic violence and TBI. As I've mentioned before, a significant portion of the domestic violence population does not return for a follow-up evaluation or outpatient therapy after the first visit and I want to know why. Why would someone refuse to take their medications or see their speech therapist? Is this the result of trauma or a preexisting problem?

I've also been working with the program coordinator who frequently handles domestic violence cases. For my first task, I've been researching the effects of drugs on the adolescent brain. This information will be presented to teens in juvenile detention to help them understand the consequences of their actions (and maybe scare them just a tiny bit). The second task is to compile information for an educational pamphlet on different brain injuries (TBI, stroke, aneurysm, etc.) and translate it into Spanish. These handouts will be written at a 3rd grade reading level in both Spanish and English to help educate the local population about these conditions. By making this information easily accessible to everyone, regardless of ethnicity or education level, people can learn how to recognize the symptoms of brain injury and prevent further damage. Neither of these tasks are directly related to my own research, but this kind of teaching could also be applied to the domestic violence population. Throughout my research, I will have to consider the different opinions and histories of each patient, especially since my data relies on self-reported numbers. Additionally, I would like to analyze how other factors (i.e. age or ethnicity) might affect the rate of progress in domestic violence victims. I hope to officially start with data collection in the coming week.