Student’s Name
Institution Affiliation
 
 
 
 
 
 
 
 
 
 
 
Analysis of Variance (ANOVA) in Biostatistics
Analysis of variance (ANOVA) refers to an array of methods used to measure variations of a group of data from the mean (Rutherford, 2011). Ideally, the simplest form of ANOVA is used to test three population means. In general, ANOVA entails the division of the variations into two components, after which, the analyst evaluates the contribution of each part to the total variation (Howell, 2014). Overly, ANOVA is founded on the assumptions that the data is normally distributed, it has a constant variance, and there are independent simple random samples. Further, it has two hypothesis as follows:

  • H0: all means are equal.
  • HA: not all means are equal (Cardinal & Airtken, 2013).

Use of ANOVA in Biostatistics
Walker, O., Thompson, E., Hill, S., Holton, K., Bodger, K., & Pearson, M. (2015). Commissioning for COPD care: A new, recordable metric that supports the patient interest. Journal of Public Health, 38(2), 396-402.
Research
Data on health care has a significant impact on the improvement in the provision of medical services. Basically, this research aimed at identifying the frequency and health burden of chronic obstructive pulmonary disease (COPD) on primary, secondary, and integrated care services. Research was conducted using “hospital episode statistics” for “bed days/1000 population” in 150 UK Primary Care Trusts (PCTs) facilities in 2006-2007 and 2007-2008. Noteworthy, the data focused on COPD prevalence. Moreover, the analysts checked on the factors that influenced these variations.
Results of the Study
During the 2006-2008 period, there were 248,996 COPD admissions. Overly, the “bed days/1000 population” were consistent for the periods 2006-2007 and 2007-2008 since their correlation was (r=0.87; P<0.001). Markedly, the difference in emergency admission rate (P<0.001), the size of emergency admission due to COPD (P<0.001), as well as the short duration of hospital stay at (P<0.001) led to a >2-fold difference between the best and worst performing PCTs.
Research Findings
Walker et al. (2015), found that COPD care requires a coordinated approach at both the hospital and community level. In light of this, there is a need for integrated care service so that patients have assistance in these locations. Notably, bed days/ 1000 PCT population indicated that the current health care is variable and can be effectively measured. Consequently, it is a useful metric for measuring the quality of COPD healthcare service.
Conclusion
To sum up, the use of ANOVA is important in the evaluation of large data in a healthcare system. Primarily, the use of ANOVA enables researchers to have a quick and unbiased method of studying various health issues. In effect, the use of ANOVA in biostatistics enables healthcare facilities to develop realistic and sound policies.
 
 
References
Cardinal, R., & Aitken, M. (2013). ANOVA for the behavioral sciences researcher. Mahaw, NJ: Lawrence Erlbaum Associates Inc. Publishers.
Howell, D. (2014). Fundamentals of statistics: For the behavioral sciences (9th Ed.). New York, NY: Cengage Learning.
Rutherford, A. (2011). ANOVA and ANCOVA: A GLM approach (2nd Ed.). Hoboken, NJ: John Wiley and Sons.
Walker, O., Thompson, E., Hill, S., Holton, K., Bodger, K., & Pearson, M. (2015). Commissioning for COPD care: A new, recordable metric that supports the patient interest. Journal of Public Health, 38(2), 396-402.