Random Bias vs. Systemic Bias

I tried to keep this short and limited the controversial elements as this was a topic of discussion in my nursing research class:

Random and systemic bias can occur any time in any study. Early apology for ranting but this is my pet peeve. Surprisingly, bias has been shown to exist too often in renowned journals such as the Journal of the American Medical Association (JAMA). This problem has been building upon itself for decades (Freedman, 2011). As a result, we have been travelling down a road filled with iatrogenic injury and death. Amazingly enough, this has also been reported in JAMA (Starfield, 2000) with medical care listed as the third leading cause of death in the United States.  Starfield (2000) points out that complexities related to this problem have multiple factors of which we all agree. Unfortunately, what we cannot count out are the implications of EBP from historically biased research data. Freedman also notes that biased research has helped shape modern standards of practice such as hormone replacement therapy, coronary stents, and low-dose aspirin used to mitigate cardiovascular events. According to Freedman (2011) Dr. John Ioannidis has demonstrated a 40% error rate in 49 of the most respected and cited research articles in the last 13 years.
When it comes to real-world practical prevention of bias, it is extremely difficult to eliminate in the medical field since research is often driven from inventions that incorporate pharmaceutical or mechanical means and funding sources often drive the research. Nursing tends to follow medical, so we are just as vulnerable. Even the gold standard, randomized trials, has a reported rate of 25% inaccuracies due to bias (Freedman, 2011).
Random bias occurs when a participant or researcher makes an assumption about data (Polit & Beck, 2012). The assumption is not an accurate representation of what is or has occurred in the study (Peterson, 2009). One example is food diaries. They have been reported to often be inaccurate. People forget and underrepresent consumption or over represent their food intake. One strategy to reduce bias here would be to photograph every meal. The researcher could review the diary, make comparisons with pictures and have tighter correlation with reality.
Systemic bias is a little different. One example is The China Study. The primary researcher Dr. T. Colin Campbell conducted the study under the premise that there was something inherently harmful with consuming animal protein. The data he presented in his book revealed this correlation (Campbell & Campbell, 2006). Further analysis of the data reveals an inverse correlation with animal protein consumption and other foods were found to correlate positively with all cause mortality, (Minger, 2011). How do we avoid this type of bias? The solution may be three-fold: Utilization and  re-utilization of multiple null-hypotheses based upon ongoing collection of data and representation of all data positive, negative, and ambiguous.
During any study we need to remain vigilant in order to reduce random or systemic bias. Multiple contrarian viewpoints should be integrated as this my help identify confounding factors or other variables in need of deeper analysis.
Bogl, L., Kaprio J., Korkeila, M., Pietilainen, K., Rissanen, A., Westerterp, K., and Yki-Jarvinen, H. (2010). Inaccuracies in food and physical activity diaries of obese subjects: complementary evidence from doubly labeled water and co-twin assessments. International Journal of Obesity. Mar;34(3):437-45.
Campbell, T. and Campbell, T. (2006. The China Study: Startling implications for diet, weight loss, and long-term health. Dallas, TX: BenBella Books
Freedman, D. (2011). Lies, Lies, Damned Lies, and Medical Science. The Atlantic Monthly. Retrieved February 6, 2012 from http://www.theatlantic.com/magazine/archive/2010/11/lies-damned-lies-and-medical-science/8269/
Minger, D. (2011). The China Study, wheat, and heart disease; [sic] Oh my! Raw Food SOS: Troubleshooting on the raw food diet. Retrieved February 18, 2012 from rawfoodsos.com/category/china-study/
Peterson, (2009). The Mathematical Tourist. Mathematical Association of America. Retrieved February 18, 2012 from http://www.maa.org/mathtourist/mathtourist_11_08_09.html
Polit, D. and Beck, C. (2012). Nursing research: Generating and assessing evidence for nursing practice [9th ed.]. Philadelphia, PA: Lippincott Williams & Wilkins.
Starfield, B. (2000). Is US Health Really the Best in the World? The Journal of American Medical Association. 284(4):483-485. doi: 10.1001/jama.284.4.483