Introductory Biostatistics/Biostatistics Concepts & Applications

Using the data described in the above table answer the Questions 1-3 below.          

Question 1 [5 marks]: Perform appropriate statistical analysis and evaluate the strength of relationship of HrQoL with patients’ age, duration of diabetes and number of complications.

Question 2 [30 marks]: Perform appropriate simple as well as multiple regression analyses with stepwise variable selection method (use backward elimination method) to find the variables (from the list in Table 1 above) those are significantly related to HrQoL. For this analysis make an initial assumption that HrQoL approximately follows the normal distribution, i.e., you do not need to evaluate pre-analysis normality of HrQoL.

Present the above simple and stepwise multiple regression analyses results in a table (follow Module 8 Formative Assessment (FA 8.3) - Model Question) and interpret the beta coefficients and 95% CI of both simple and multiple regression for the variables duration of diabetes and depression only. Then provide a summary discussion of the results followed by a conclusion and implication. Address all other (if any) relevant issues/results in your presentation.

Note: (1) for presentation please follow all necessary steps discussed in lecture; (2) please do not repeat the steps for each variable – follow Question 2 in AT2; (3) evaluation of model adequacy is not required for simple regression.

Consider that you shared the above analysis results with your colleague who has expertise in clinical/public health study data analysis. Your colleague recommended to adding the following variables into your multiple regression model: current hypertension status, systolic blood pressure, creatinine level (a measure of kidney function), and diastolic blood pressure. Assume that these variables are available in the database. Briefly discuss would you address this recommendation?

Question 3 [15 marks]: Perform appropriate simple as well as multiple regression analyses with stepwise variable selection method (use backward: Wald) to find the variables those are significantly related to depression. Exclude HrQoL from your analysis. Present the results in a table (follow the Formative Assessment in Module 10) and provide a summary discussion of the results followed by a conclusion. Address all other (if any) relevant issues/results in your presentation.

Using the above results, predict the risk of depression for a physically active patient who completed graduate degree and have five complications, and also have anxiety and impaired cognitive function.

Note: (1) for presentation please follow all necessary steps discussed in lecture; (2) please do not repeat the steps for each variable – follow Question 2 in AT2.

Note: For Questions 4 & 5 you do not need to follow the steps outlined in the lecture and/or tutorial.

 Question 4 [25 marks]:                                                                                                               

Objectives: To examine the effect of different stages of chronic kidney disease (CKD) on patients’ risk of post-operative mortality and complications following isolated coronary artery bypass grafting (CABG) in a large cohort of patients who had cardiac surgery.

Description: All patients who underwent isolated CABG in the cohort were reviewed, and their preoperative glomerular filtration rates (eGFR) were estimated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation.

The CKD stages were classified as follows: normal: eGFE ≥ 90 ml/min/1.73m² and not on dialysis, mild: eGFR 60-89 ml/min/1.73m² and not on dialysis, moderate: eGFR 30 - 59 ml/min/1.73m² and not on dialysis, severe: eGFR < 30 ml/min/1.73m² and not on dialysis; and dialysis dependent.

Analysis Method: The descriptive statistics for various post-operative outcomes were reported as percentages (see Table 2). The effect of CKD stages on each of the outcomes following isolated CABG were examined using multiple logistic regression method. In the multiple logistic regression analysis the CKD variable was adjusted for other 12 predictors (please see the list below the Table 3), i.e., there were 13 predictors in each of the regression models including CKD stages. However, the OR, 95% CI and p-value were reported only for CKD stages (see Table 3). Normal CKD stage was considered as the reference category in the multiple logistic regression analysis. Thus, the ORs in the Table 3 quantify the odds of various CKD stages (moderate to severe) as compared to normal CKD stage. Please see the Appendix for a brief description of post-operative mortality and complications.

Discuss the results in Tables 2 and 3 and make a summary conclusion followed by the impact of the findings. Your answer must have only the following three separate sections:

• Section 1: Summary (overall) discussion of descriptive statistics (presented in Table 2) of post-operative outcomes by CKD
• Section 2: Summary (overall) discussion of multiple logistic regression analysis results presented in Table
• Section 3: Make a brief summary conclusion about the effect of CKD on post-operative mortality and complications (see column 1 in Table 3 for the list of these variables) followed by the impact of the