REGRESSION EQUATION AND SUMMARY OF RECENT RESEARCH 7
Name of the Student
Name of the Institution
Example of a Quantitative Study using Regression Analysis
Childhood obesity is currently one of the major health concerns inthe US. The high rate of obesity among children aged below 12 yearshas been attributed to the consumption of energy-dense foods (Maheret al., 2007). A study can be conducted to confirm whether the highrate of obesity among children who are below 12 years is associatedwith the consumption of energy-dense foods. The National Health andNutrition Examination has been conducting surveys on the consumptionbehaviors and the causes and rates of obesity and otherhealth-related problems. I would use the national representative datacollected by the organization to determine whether there issignificant association between the consumption of energy-dense foodsand the high rate of obesity among children who are below 12 years. Iwould use quantitative data on dietary energy density and thepredictors of obesity (measures of Body Mass Index and waistcircumference). I would apply regression models to the variables todetermine whether there is significant association between dietaryenergy density and the predictors of obesity.
Methods of Selecting Variables into the Regression Equation
During the process of selecting variables into the regressionequation, I would use either the forward inclusion method or thestepwise-selection multiple regression. The forward inclusion methodinvolves addition one independent variable at a time to theregression (Sawilowsky, 2007). I would select one predictor ofobesity that I expect to have the highest correlation with thedietary energy density and put it first into the equation. Then, Iwould then conduct regression analysis with just the selectedvariable. I would check whether dietary energy density hassignificant association with the variable. I would then repeat thesame process with the other variables, each at a time. Alternatively,I can use the stepwise-selection multiple method. Thestepwise-selection multiple regression is almost similar to theforward inclusion method (Sawilowsky, 2007). In the forward inclusionmethod, a variable that is added to the regression equation remainsthere during the subsequent regressions. However, in thestepwise-selection multiple regression, a variable that has nosignificant contribution to the equation is not included in thesubsequent calculations. The two methods would help to determinewhether each of the three predictors of obesity has significanceassociation with dietary energy density (Veney, Kros & Rosenthal,2009).
A critical analysis and application of this study
Over the past one decade, the rate of obesity among children has beenrising rapidly. Studies have linked the problem to lifestylebehaviors (Maher et al., 2007). In particular, studies have linkedthe high rate of obesity among the children to the consumption ofenergy-dense foods. In most cases, energy-dense foods, high in addedfats, added sugars and refined grains are inexpensive and palatable.However, they are associated with poor diet quality and high level ofenergy intake. Such foods have been found to contribute to obesity(Maher et al., 2007). However, the link between the consumption ofenergy-dense foods and the development of obesity is not wellunderstood. Whereas some studies have found significant associationbetween the two, others have not. The findings of the study would beuseful to add more evidence on the existing knowledge. If the studywould show significant association between the independent anddependent variables, the findings would be useful to the public,health professionals and organizations that campaign againstlifestyle behaviors that may lead to obesity.
Summary of Recent Research
Mongkolsomlit, S., Patumanond, J., Tawichasri, C., Komoltri, C. &Rawdaree, P. (2012). Meta
Regression of Risk Factors for Microalbuminuria in Type 2 Diabetes.Southeast Asian J
Trop Med Public health, 43(2), 445-466
Mongkolsomlit et al. (2012) conducted a study to determinethe risk factors that are associated with microalbuminuria inpatients with type 2 diabetes. Mongkolsomlit et al. (2012) analyzed22 previous empirical studies related to the study topic andconducted a meta-regression analysis on the risk factors andmicroalbuminuria. As well, the researchers applied the random effectmodel to obtain pooled odd ration estimates. Mongkolsomlit et al.,(2012) found four risk factors to have significant association withmicroalbuminuria, namely smoking, uncontrolled hypertension, poorglycemic control and central obesity. Mongkolsomlit et al. (2012)concluded that there is need for establishment of health promotionprograms in order to mitigate the risk factors in patients with type2 diabetes.
The null hypothesis and alternative hypotheses
The null hypothesis (H0): Smoking, age, gender, uncontrolled bloodpressure, dylispidemia, uncontrolled hypertension, duration ofdiabetes, poor glycemic control, and central obesity (body massindex) do not have significant association with microalbuminuria inpatients with type 2 diabetes.
Alternative hypothesis (H1): Smoking, age, gender, uncontrolled bloodpressure, dylispidemia, uncontrolled hypertension, duration ofdiabetes, poor glycemic control, and central obesity (body massindex) have significant association with microalbuminuria in patientswith type 2 diabetes.
Regression Analysis Results
Mongkolsomlit et al. (2012) applied meta-regression analysis to theidentified risk factors and microalbuminuria. The analysis found thatout of the risk factors identified in the previous studies, smoking,uncontrolled hypertension, poor glycemic control and central obesitywere the only risk factors that were significantly associated withmicroalbuminuria in patients with type 2 diabetes. The regressionresults for smoking, uncontrolled hypertension, poor glycemic controland central obesity were 1.37, 95% CI 0.95-1.98 OR 1.26, 95% CI1.08-1.46 0.79, 95% CI, 0.63-0.99 and 1.43, 95% CI, 1.14-1.80,respectively (Mongkolsomlit et al., 2012).
Type of Data Required and the Assumptions of the Test
The study needed to use quantitative data on the results of theprevious studies examined. The researchers needed to extract datafrom previous cohort studies, case-control studies and analyticalcross-sectional studies. The main assumption of the study was thatthe there was a high level of heterogeneity in the findings derivedfrom previous studies. The researchers found significant level ofheterogeneity inn the results of the previous studies.
Whether the Study is Statistically Significant
Mongkolsomlit et al. (2012) conducted tests for statisticalsignificance of the level of heterogeneity in the studies from whichthe data was derived. The results indicated that the heterogeneity inthe previous studies, with regard to smoking and central obesity,were not statistically significant (p< .05). The study found astatistically significant level of heterogeneity with regard in theresults for uncontrolled hypertension and poor glycemic control (p<.05).
Statistical significance refers to the probability that an outcomehas a high probability of being true and that it has not occurred dueto chance (Veney et al., 2009).
The Possible Implications of the Study
The previous studies on the risk factors associated withmicroalbuminuria have found mixed and controversial results. Themeta-regression analysis by Mongkolsomlit et al. (2012) highlightedthe overall effect specific risk factors examined by previousresearchers. The study harmonized the findings from previous studiesand it identified the specific risk factors that have significantimpact on the development of microalbuminuria in patients with type 2diabetes. In addition, previous studies have shown thatmicroalbuminuria causes cardiovascular and nephropathic complicationsin persons with type 2 diabetes. Mongkolsomlit et al. (2012)highlighted the importance of avoiding health behaviors that may leadto the development of microalbuminuria and the associatedcomplications.
Jekel, J. F. (2007). Epidemiology, Biostatistics, and PreventiveMedicine. London: Elsevier
Maher, E. J., Li, G., Carter, L. & Johnson, B. D. (2007).Preschool Child Care Participation and
Obesity at the Start of Kindergarten. Pediatrics, 122 (2), 322-330
Sawilowsky, S. S. (2007). Real Data Analysis. New York, NY:IAP
Veney, J. E., Kros, J. F. & Rosenthal, D. A. (2009). Statisticsfor Health Care Professionals:
Working With Excel. California: John Wiley & Sons