Clinical Trials of OxyContin OxyContin (oxycodone) is a drug used to treat pain, but it is well known for its addictiveness and danger. In a clinical trial, among subjects treated with OxyContin, 52 developed nausea and 175 did not develop nausea. Among other subjects given placebos, 5 developed nausea and 40 did not develop nausea (based on data from Purdue Pharma L.P.). Use a 0.05 significance level to test for a difference between the rates of nausea for those treated with OxyContin and those given a placebo.
c. Does nausea appear to be an adverse reaction resulting from OxyContin?
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Step 1: Define the null and alternative hypotheses. The null hypothesis (H₀) states that there is no difference in the rates of nausea between the OxyContin group and the placebo group. The alternative hypothesis (H₁) states that there is a difference in the rates of nausea between the two groups.
Step 2: Calculate the proportions of nausea for each group. For the OxyContin group, the proportion is calculated as p₁ = 52 / (52 + 175). For the placebo group, the proportion is calculated as p₂ = 5 / (5 + 40).
Step 3: Compute the pooled proportion (p̂) using the formula: p̂ = (x₁ + x₂) / (n₁ + n₂), where x₁ and x₂ are the number of successes (nausea cases) in each group, and n₁ and n₂ are the total sample sizes for each group.
Step 4: Calculate the standard error (SE) for the difference in proportions using the formula: SE = √[p̂(1 - p̂)(1/n₁ + 1/n₂)].
Step 5: Compute the test statistic (z) using the formula: z = (p₁ - p₂) / SE. Then, compare the test statistic to the critical value for a significance level of 0.05, or use the p-value approach to determine whether to reject the null hypothesis. Interpret the results to assess whether nausea appears to be an adverse reaction resulting from OxyContin.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Hypothesis Testing
Hypothesis testing is a statistical method used to determine whether there is enough evidence to reject a null hypothesis in favor of an alternative hypothesis. In this context, the null hypothesis would state that there is no difference in the rates of nausea between the OxyContin and placebo groups, while the alternative hypothesis would suggest that a difference exists. The process involves calculating a test statistic and comparing it to a critical value based on a chosen significance level.
The significance level, often denoted as alpha (α), is the threshold for determining whether the results of a hypothesis test are statistically significant. In this case, a significance level of 0.05 indicates that there is a 5% risk of concluding that a difference exists when there is none (Type I error). If the p-value obtained from the test is less than 0.05, the null hypothesis can be rejected, suggesting that nausea may be an adverse reaction to OxyContin.
A contingency table is a type of data representation that displays the frequency distribution of variables, allowing for the analysis of the relationship between them. In this scenario, the table would show the number of subjects who developed nausea versus those who did not, categorized by treatment type (OxyContin vs. placebo). This format is essential for calculating proportions and conducting tests like the Chi-square test to assess the association between treatment and nausea occurrence.