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FINAL INFERENTIAL SET Instructions: Please create one document for all of your answers and work for this project. This will be the document that you submit to the link in week 10 as your official assignment. (Should you create an Excel Workbook, for example, create as many additional tabs [pages] as necessary for each individual problem. Please be sure to correctly label each additional tab so it will be easy to identify the corresponding exam problem). As a reminder, if you do not provide documented work for the appropriate problems, it could result in points being deducted from your overall grade. Additionally, should simple parenthetical references/citations be necessary to heighten your analyses, you are expected to include them. You are expected to work on this exam individually and to submit it by the due date and time listed on your syllabus. Best of luck for a successful completion! PART 1: According to IARC Monographs on the Evaluation of Carcinogenic Risks to Humans: Tobacco smoke and involuntary smoking (Vol. 83. 2004, Lyon: IARCPress), people with the highest lung cancer risks are those who: • Smoke the most cigarettes per day • Smoke over long periods of time, and • Start smoking young We cannot yet exactly calculate a person’s lung cancer risk based on how many cigarettes they smoke or the number years they have been a smoker. Yet studies have shown that lung cancer risk is greatest among those who smoke the most cigarettes over the longest period of time. As such, altering this hypothesis slightly, you and your team collect the following data to demonstrate whether or not there is an existing evidence-based relationship between the length of smoking time and visits to a clinician based on thoracic symptom-related issues. After reviewing five years worth of randomized charts of self-proclaimed smokers in a hospital system based in Philadelphia, you identify the following data: 14 6 44 7 35 12 21 7 41 11 4 2 21 2 40 5 5 1 21 3 27 8 10 2 19 2 29 1 10 3 15 1 30 9 13 3 *Please note that this is a random list of data that does not include all of the patient charts that were reviewed for this study. 5A) Create a scatter plot diagram of the data provided. 5B) Provide evidence on whether there is a strong, weak, or non-existent correlation between the two variables? 5C) Provide evidence on whether there is a strong or weak regression (predicted pattern of Length of years smoking # Of visits to a clinician due to thoracic issues Length of years smoking # Of visits to a clinician due to thoracic issues Length of years smoking # Of visits to a clinician due to thoracic issues success over time)? 5D) In your own words, what is your analysis of these results? PART 2: The owner of a laboratory wants to keep sick leave as low as possible by keeping employees healthy through disease prevention programs. Many employees have contracted pneumonia leading to productivity problems due to sick leave from the disease. There is a vaccine for pneumococcal pneumonia, and the owner believes that it is important to get as many employees vaccinated as possible. Due to a production problem at the company that produces the vaccine, there is only enough vaccine for half the employees. In effect, there are two groups; employees who received the vaccine and employees who did not receive the vaccine. The company sent a nurse to every employee who contracted pneumonia to provide home health care and to take a sputum sample for culture to determine the causative agent. They kept track of the number of employees who contracted pneumonia and which type of pneumonia each had. The data were organized as follows:  Group 1: Not provided with the vaccine (unvaccinated control group, N = 84)  Group 2: Provided with the vaccine (vaccinated experimental group, N = 84) In this case, the independent variable is vaccination status (vaccinated versus unvaccinated). The dependent variable is health outcome with two levels:  Contracted pneumoccal pneumonia;  Did not contract pneumonia. The company wanted to know if providing the vaccine made a difference. To answer this question, they must choose a statistic that can test for differences when all the variables are nominal. The χ2 statistic was used to test the question, “Was there a difference in incidence of pneumonia between the two groups?” At the end of the winter, Table 1 was constructed to illustrate the occurrence of pneumonia among the employees. Table 1. Results of the vaccination program. Health Outcomes Unvaccinated Sick w/ pneumococcal pneumonia 23 No pneumonia 61 Total ? 6A) What are your null and alternate hypotheses? 6B) What is your observed calculated Chi square result? 7A) What is the degree of freedom? 7B) What is the start of your critical region? 7C) Do you reject your null hypothesis? 7D) In your own words, what is your analysis of this result? Vaccinated Total 7 ? 77 ? ? ? PART 3: Based on the positive null hypothesis results of the carcinoid tumor attention study discussed in the midterm project, the team of AP oncology nurses and pathologists presented an evidence- based report to oncologists at an annual national clinical oncology symposium. While the null hypothesis was proven to be correct, the primary endpoint of presenting this research was to engage oncologists in being more attentive to correctly diagnosing and prescribing treatment for those patients who have recurring carcinoid tumors. As such, oncologists determined that the best treatments to be used are streptozotocin, 5-fluorouracil (5-FLU), dacarbazine (DTIC), Adriamycin, and only a few other options. As a follow-up to the nurses' research, a group of research oncologists therefore decided to identify patients and ask them to participate in a 5- year survival study utilizing DTIC. The study started with 80 patients. Between the first and second years (full-year 1), 0 patients had died but 4 were censored. Between the second and third years (full-year 2), 2 patients had died and 0 were censored. Between the third and fourth years (full-year 3), 0 patients had died and also 0 were censored. Between the fourth and fifth year (full-year 4), 32 patients had died and 0 were censored. Between the fifth and sixth years (full-year 5), 6 more patients died yet 0 were censored. 8A) After 5 years, what is the survival percentage for patients with carcinoid undergoing DTIC treatment? 8B) When applying a 90% confidence to the test results, what is the confidence interval? 8C) In your own words, what is your analysis of this survival test? Be sure to include an analysis of the patient as well, or in other words, when and why do you assume patients start to rapidly decline. PART 4: You have been selected to be part of a research team in your facility. Due to a possible batch of defective medicine X, your team has been asked to randomly select patients already receiving the batch to determine if treatment is causing recurrences of nausea. Your team has selected to separate patients into 3 groups: those who receive one batch within a week, those who receive two batches within a week, and those who receive three batches within the week. Your team decides to make the claim that at least one of your groups will have a noticeably different mean than the other groups. Group 1 35 35 28 65 57 53 22 66 24 30 56 Group 2 21.5 102 22.2 60 20.6 72 23.9 96 17.9 60 21.9 20.2 Group 3 48 96 24 78 20.4 60 18.1 23.6 30.7 9A) What is the Null hypothesis of this study 9B) What is the Alternate hypothesis of this study? 9C) What is the Grand Mean? 9D) What is the Between Group Variance? 9E) What is the Within Group Variance? 9F) What is the F Test Result? 9G) Do you or do you not reject the null hypothesis? 9H) In your own words, what is your analysis of these results. PART 5: Using the graph below, please describe the situation to a colleague who may not have studied biostatistics.

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Part 5
Two groups of people (patients) were followed for 3 years and the deaths were recorded. The groups were different by one group having gene A and the other group gene B. Survival was recorded and presented as percentage of live members of the group during the time period. ‘We can see that at the beginning of the following both group had all alive members presented as 100% of percent survival. During time course some of the group members died and percent of survival started to decline below 100%. For example, after 2 years, 50% of group with gene B signature were dead (and 50% alive), while group with gene A signature was 80% alive at the same time (20% dead). From the graph we can see and conclude that the survival was different between gene A and B during 3 years. The gene B signature was linked with lower survival (higher death rate). ...

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