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.
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