The following data are from a study on Botox injections. Patients received a high-dose injection in one eye (experimental treatment = treatment E) and a low-dose injection in the other eye (control treatment = treatment C). Patients were asked to rate the level of pain in each eye on a 1-10 scale, with higher scores indicating more pain. The assignment of treatments to eyes was randomized. The subjects came back over several visits. Data from the last visit are given in the table below. Effect of Botox injection on eye pain Pain score at the last visit

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**Ophthalmology Study: Effect of Botox Injection on Eye Pain**

The following data are from a study on Botox injections. Patients received a high-dose injection in one eye (experimental treatment = treatment E) and a low-dose injection in the other eye (control treatment = treatment C). Patients were asked to rate the level of pain in each eye on a 1–10 scale, with higher scores indicating more pain. The assignment of treatments to eyes was randomized. The subjects came back over several visits. Data from the last visit are given in the table below.

| Subject | Pain in E eye | Pain in C eye |
|---------|--------------|--------------|
| 1       | 1.3          | 8.7          |
| 2       | 7.3          | 1.4          |
| 3       | 0.0          | 0.9          |
| 4       | 0.0          | 9.6          |
| 5       | 3.0          | 7.9          |
| 6       | 0.0          | 9.1          |
| 7       | 3.5          | 5.1          |
| 8       | 0.0          | 2.2          |
| 9       | 0.0          | 2.6          |
| 10      | 2.0          | 7.9          |
| 11      | 0.0          | 4.4          |
| 12      | 3.0          | 4.6          |
| 13      | 5.0          | 9.1          |
| 14      | 0.3          | 7.4          |
| 15      | 0.0          | 0.4          |
| 16      | 4.3          | 0.7          |

**Explanation**: The table lists the pain scores for two eyes per subject, where "Pain in E eye" refers to the eye that received the high-dose injection and "Pain in C eye" refers to the eye with the low-dose injection. The scores represent the level of pain experienced, with a scale from 0 (no pain) to 10 (severe pain), recorded at the last visit for each subject.

**Instructions**: The lower part of
Transcribed Image Text:**Ophthalmology Study: Effect of Botox Injection on Eye Pain** The following data are from a study on Botox injections. Patients received a high-dose injection in one eye (experimental treatment = treatment E) and a low-dose injection in the other eye (control treatment = treatment C). Patients were asked to rate the level of pain in each eye on a 1–10 scale, with higher scores indicating more pain. The assignment of treatments to eyes was randomized. The subjects came back over several visits. Data from the last visit are given in the table below. | Subject | Pain in E eye | Pain in C eye | |---------|--------------|--------------| | 1 | 1.3 | 8.7 | | 2 | 7.3 | 1.4 | | 3 | 0.0 | 0.9 | | 4 | 0.0 | 9.6 | | 5 | 3.0 | 7.9 | | 6 | 0.0 | 9.1 | | 7 | 3.5 | 5.1 | | 8 | 0.0 | 2.2 | | 9 | 0.0 | 2.6 | | 10 | 2.0 | 7.9 | | 11 | 0.0 | 4.4 | | 12 | 3.0 | 4.6 | | 13 | 5.0 | 9.1 | | 14 | 0.3 | 7.4 | | 15 | 0.0 | 0.4 | | 16 | 4.3 | 0.7 | **Explanation**: The table lists the pain scores for two eyes per subject, where "Pain in E eye" refers to the eye that received the high-dose injection and "Pain in C eye" refers to the eye with the low-dose injection. The scores represent the level of pain experienced, with a scale from 0 (no pain) to 10 (severe pain), recorded at the last visit for each subject. **Instructions**: The lower part of
**Section of an Educational Website: Comparing Pain Scores in Clinical Trials**

**Statistical Hypothesis Testing: A Practical Example**

In clinical trials, comparing the effectiveness of treatments often involves statistical hypothesis testing. This section explores how to analyze differences in pain scores between two treatments labeled "E" and "C" by examining hypothesis testing methodology and providing step-by-step guidance.

### Hypothesis Testing Outcomes

1. **Reject \( H_0 \):** Sufficient evidence exists to conclude that, on average, the pain scores in the E-treated eye are different from those in the C-treated eye.
2. **Fail to Reject \( H_0 \):** Insufficient evidence exists to conclude a significant difference in average pain scores between the E and C eyes.

### Percentage Comparison Method

An alternative comparison involves examining the percentage of subjects reporting less pain in the E-treated eye versus the C-treated eye. 

### Choosing the Right Statistical Test

(C) **Selecting the Statistical Test**

- **One-sample binomial test—exact method**: This test is most appropriate when comparing the percentage of subjects with less pain in the E eye against a constant proportion expected under the null hypothesis.

### Performing the Test

(D) **Executing the Statistical Test**

For a two-tailed test with \(\alpha = 0.05\), you will:

1. **State Hypotheses:**
   - Null Hypothesis \( H_0: p = 0.5 \)
   - Alternative Hypothesis \( H_1: p \neq 0.5 \)

2. **Finding the Test Statistic:**
   - If no test statistic is defined, mark as "DNE" (Does Not Exist).

3. **Using Technology to Calculate the p-value:**
   - Follow specific software or calculators to determine the exact p-value, rounded to four decimal places.

4. **Stating the Conclusion:**
   - **Fail to reject \( H_0 \):** Insufficient evidence to assert a difference in pain reduction between the E eye and expected outcomes if treatments were comparable.
   - **Reject \( H_0 \):** Indicating the proportion of pain reduction in the E eye differs from expectations under treatment equivalence.

**Notes:**

- The responses and methods outlined above are accurate, but difficulties arise in obtaining the correct p-value. Please refer to technology guides or statistical software documentation for precise calculation procedures.

This session covers essential insights into hypothesis testing and sets the
Transcribed Image Text:**Section of an Educational Website: Comparing Pain Scores in Clinical Trials** **Statistical Hypothesis Testing: A Practical Example** In clinical trials, comparing the effectiveness of treatments often involves statistical hypothesis testing. This section explores how to analyze differences in pain scores between two treatments labeled "E" and "C" by examining hypothesis testing methodology and providing step-by-step guidance. ### Hypothesis Testing Outcomes 1. **Reject \( H_0 \):** Sufficient evidence exists to conclude that, on average, the pain scores in the E-treated eye are different from those in the C-treated eye. 2. **Fail to Reject \( H_0 \):** Insufficient evidence exists to conclude a significant difference in average pain scores between the E and C eyes. ### Percentage Comparison Method An alternative comparison involves examining the percentage of subjects reporting less pain in the E-treated eye versus the C-treated eye. ### Choosing the Right Statistical Test (C) **Selecting the Statistical Test** - **One-sample binomial test—exact method**: This test is most appropriate when comparing the percentage of subjects with less pain in the E eye against a constant proportion expected under the null hypothesis. ### Performing the Test (D) **Executing the Statistical Test** For a two-tailed test with \(\alpha = 0.05\), you will: 1. **State Hypotheses:** - Null Hypothesis \( H_0: p = 0.5 \) - Alternative Hypothesis \( H_1: p \neq 0.5 \) 2. **Finding the Test Statistic:** - If no test statistic is defined, mark as "DNE" (Does Not Exist). 3. **Using Technology to Calculate the p-value:** - Follow specific software or calculators to determine the exact p-value, rounded to four decimal places. 4. **Stating the Conclusion:** - **Fail to reject \( H_0 \):** Insufficient evidence to assert a difference in pain reduction between the E eye and expected outcomes if treatments were comparable. - **Reject \( H_0 \):** Indicating the proportion of pain reduction in the E eye differs from expectations under treatment equivalence. **Notes:** - The responses and methods outlined above are accurate, but difficulties arise in obtaining the correct p-value. Please refer to technology guides or statistical software documentation for precise calculation procedures. This session covers essential insights into hypothesis testing and sets the
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