Reference: Wang, L., et al., Quality of life and the relevant factors in patients with chronic hepatitis B. Hepatogastroenterology, 2012. 59(116): p. 1036-42.
A little bit different of an article that I am used to publishing on this blog, but I like difference. Here is a study quantifying the quality of life factors for patients with Hepatitis B. I can’t tell you how much fun I had reading this article!
A little bit different of an article that I am used to publishing on this blog, but I like difference. Here is a study quantifying the quality of life factors for patients with Hepatitis B. I can’t tell you how much fun I had reading this article!
Brief Summary:
Hepatitis B is an infectious inflammatory disease of the liver caused by the Hepatitis B virus. Currently, little is known regarding the health-related quality of life (HRQL) factors which are affected by Hepatitis B. The main objective of this study was to access the HRQL factors for patients with Hepatitis B. The HRQL factors were physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH) from the Chinese version of the medical outcomes study 36-Item Short-Form health survey. A total of 407 patients were accrued into the study, and they were compared to a literature based control group. Secondary objectives were to ascertain the effect of genotype differences in the ACE and DRD4 genes on the HRQL factors, and also to study the effect of anti-viral therapies on some of the HRQL factors. These factors included physiology function (PHD) and psychology function (PSD)
Results:
The HRQL factors were significantly lower for the patients with Hepatitis B compared to the literature-based control group, and they were lower for all 8 previously mentioned factors. Furthermore, the genotype of the ACE and DRD4 genes were found to be associated with HRQL, and the anti-viral therapies were found to improve several HRQL factors which included physiology function and society function. Other therapies (hepatoprotective, jaundice eliminating and supportive treatment ) were not shown to add an improvement.
Implications for Practice:
Implications for Practice:
Patients with Hepatitis B have significantly lower quality of life factor scores. If the Hepatitis B patient has a highly negative physiology function psychology function, then anti-viral therapies can help improve these quality of life factors.
Discussion:
Discussion:
I found it really interesting how genotype can have such a drastic effect on the quality of life factors. I imagine this relationship would hold for other types of Gastroenterology based diseases as well. Just very fascinating.
In the discussion, the authors stated that clinical therapy has a minimal effect on the emotional state of a patient Hepatitis B. Rather, family and community care are more effective than any therapy at increasing the quality of life. All of this is probably true, and I’ve seen some past quality of life studies say the same thing.
Commentary on Statistics and Study Design:
In the discussion, the authors stated that clinical therapy has a minimal effect on the emotional state of a patient Hepatitis B. Rather, family and community care are more effective than any therapy at increasing the quality of life. All of this is probably true, and I’ve seen some past quality of life studies say the same thing.
Commentary on Statistics and Study Design:
Overall, the statistical analysis was very good. I like how the author’s performed both a uni-variate and multi-variate analysis between the factors and the main response variable which was the HRQL scores. Since the version I read was not in current print, I could not see the figures, but I assume that both the uni- and multi- variate analysis was shown. Also, it was good that the author’s pointed out the limitation with using a literature-based control group. This is a definite limitation. I didn’t see a sample size for the control group, but I’m assuming it was rather large, so this technique should have been fine.
My one major suggestion would be in the interpretation of the coefficients. I am assuming that the multi-variate table (which again, I couldn’t see) included the coefficients from the multi-variate analysis. If it did not, then it needs to be there, and there should be an explicit and detailed explanation of the coefficients. For instance, if the coefficient is -20.0 for one of the factors (let’s say physical functioning), then an explicit interpretive statement needs to be stated such as “this coefficient means that the patients in the Hepatitis B group had a physical functioning quality of life score that was 20 less those patients in the control group, and this coefficient is significantly different than 0.0.” This type of explicit and descriptive statement can really help out your non-statistician tremendously, and remember, your average Joe doctor is not a statistician.
Other than that, everything looks good. I assume the sample size was good enough.
It was good to read a paper way over in China. Thanks!
.
My one major suggestion would be in the interpretation of the coefficients. I am assuming that the multi-variate table (which again, I couldn’t see) included the coefficients from the multi-variate analysis. If it did not, then it needs to be there, and there should be an explicit and detailed explanation of the coefficients. For instance, if the coefficient is -20.0 for one of the factors (let’s say physical functioning), then an explicit interpretive statement needs to be stated such as “this coefficient means that the patients in the Hepatitis B group had a physical functioning quality of life score that was 20 less those patients in the control group, and this coefficient is significantly different than 0.0.” This type of explicit and descriptive statement can really help out your non-statistician tremendously, and remember, your average Joe doctor is not a statistician.
Other than that, everything looks good. I assume the sample size was good enough.
It was good to read a paper way over in China. Thanks!
.
Chad, PhD (Translational Bioinformatics, University of Pittsburgh), combines his love and expertise of statistics, study design and medical research to produce for you “TheGastroenterologyBlog.” Chad hopes that this resource can be helpful for both Gastroenterologists and statisticians alike.
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