Question

You are an analyst at a health care consulting company and your new client is a large hospital chain. This institution collects, on a regular basis, data about patients that received treatment in one of their hospitals, but due to lack of appropriate analytical solutions and a shortage of qualified medical analysts, the scientists could not make significant progress in obtaining useful insights. As part of the engagement, you are assigned the role to assist the scientists in analyzing the data about the patients and provide information to the medical research team. The scientists from the research institution have a list of hypotheses that they would like to test using the existing patient data. These hypotheses are based on the theories and findings from the latest medical literature. The team shared with you the data about patients with heart diseases (HEART data set in the SAS Viya for Learners platform) and you are to test the following hypotheses:

H1: The weight and cholesterol levels are correlated

H2: Men are usually more obese than women

H3: Women usually smoke less than men, but their cholesterol level is higher

H4: The blood pressure is higher for people with higher cholesterol levels

Answer 1

In medical research, hypotheses are tested using statistical methods. A health care analyst will use data analysis techniques such as correlation analysis and t-tests to investigate hypotheses related to weight, cholesterol, gender differences, and blood pressure in patients with heart diseases. The key is to ascertain if there is sufficient evidence to accept or reject these hypotheses considering the statistical significance.

Step-by-step explanation:

In medical research, hypotheses are proposed explanations or predictions that can be tested through analysis of data. In the scenario given, a health care analyst is tasked with analyzing a hospital chain's patient data to test specific hypotheses regarding heart disease, which is critically relevant as heart disease is a leading cause of mortality worldwide. Here are the steps that the analyst might undertake for each hypothesis:

• H1: Correlation analysis, likely using Pearson or Spearman correlation coefficients, will test if there is a statistical relationship between weight and cholesterol levels in patients.
• H2: Analyzing BMI (body mass index) data across genders, an independent samples t-test or Mann-Whitney U test could be used to determine if there is a significant difference in obesity rates between men and women.
• H3: A comparison of smoking rates and cholesterol levels between men and women might involve chi-square tests for categorical data, like smoking status, and independent samples t-tests for continuous data, such as cholesterol levels.
• H4: To examine the relationship between blood pressure and cholesterol levels, correlation analysis or regression could be used to assess if higher cholesterol is predictive of higher blood pressure.

While conducting these analyses, researchers must consider factors such as the LDL:HDL ratio's impact on heart disease risk. Understanding that high levels of HDLs are protective, while high levels of LDLs are detrimental, advances the analyst's approach to hypothesis testing.

Statistical rigor is essential in testing these hypotheses, where the null and alternative hypotheses will be defined for each test. A decision will then be made based on statistical evidence to either reject or fail to reject the null hypothesis, considering the significance level usually set at 0.05.