A Study Is Designed To Determine Whether The Recovery Rate ✓ Solved

A Study Is Designed To Determine Whether The Recovery Rate Of A Patien

A study aims to determine whether the recovery rate of patients after surgery could be improved by administering a newly developed drug (Drug X). The study involved 30 patients at a local hospital who agreed to participate. These patients were divided into two groups based on gender: 18 men and 12 women. Within each gender group, half of the patients were randomly selected to receive Drug X immediately after surgery, while the remaining patients did not receive the drug. The healthcare provider administering the drug was the only person aware of which patients received Drug X; neither the patients nor the nurses on the hospital floor knew which patients received the drug, making this a blinded study.

The recovery rate for each patient was measured by the number of hours after surgery until the patient was able to walk around the hospital floor. The study compared the average recovery times between patients who received Drug X and those who did not, within each gender group, as well as between genders overall.

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This study involves several key elements related to research design and statistical analysis. The first step is to identify the variables involved and their measurement levels, then analyze the sampling technique, understand the study design concepts, recognize lurking variables, and evaluate the statistical soundness of the study.

1. Identification of Variables and Their Levels of Measure

Quantitative Variables:

• Recovery time (hours): The number of hours after surgery until the patient can walk around the hospital floor. This is a ratio level variable because it involves a natural zero point (zero hours) and meaningful numerical differences.

Qualitative Variables:

• Gender: Whether the patient is male or female. This is a nominal level variable because categories are named without any inherent order.
• Drug administration (received Drug X or not): Whether a patient received the drug. This is a nominal variable, as it classifies patients into two categories.

2. Sampling Technique and Justification

The study employed a random sampling within each gender group to assign patients to treatment and control groups. Specifically, random selection determined which patients within each gender received Drug X. This approach is a form of randomized block design, because the population was divided into blocks (men and women) to control for gender-related variability. Randomly selecting within these blocks reduces bias and allows for more precise estimation of treatment effects. This method ensures an unbiased comparison between those who received the drug and those who did not, within each gender.

3. How the Key Terms Relate to the Study

• a. Randomized block design: The patients were divided into blocks based on gender, and then within each block, patients were randomly assigned to treatment (Drug X) or control (no Drug X). This design accounts for variability between genders and improves the accuracy of the study.
• b. Double-blind experiment: Both the patients and the nurses were unaware of who received Drug X, and only the administrator knew the assignments. Although the question states "the doctor administering the drug was the only person aware," the design resembles a double-blind setup, which minimizes bias.
• c. Placebo effect: Since patients and nurses did not know who received the drug, any psychological effects associated with expecting recovery improvements (placebo effect) were minimized, ensuring that observed differences are more likely due to the drug's efficacy rather than psychological factors.
• d. Control group: The patients who did not receive Drug X serve as the control group, providing a baseline to compare the effects of the new drug.
• e. Treatment group: The patients who received Drug X form the treatment group, used to assess the drug's effect on recovery time.
• f. Replication: The study's replication would involve repeating the experiment with different patient populations to verify whether the findings are consistent and reliable across different settings.

4. A Lurking Variable and Its Impact

A potential lurking variable in this study is the patients' overall health status or pre-existing conditions. Patients with better general health or fewer comorbidities may naturally recover faster regardless of drug administration. If such factors are unevenly distributed between treatment and control groups, they could confound the results, falsely attributing faster recovery to Drug X. For example, healthier patients might be more likely to be assigned or might happen to be in the treatment group even if randomization is not perfect, skewing the observed effect of the drug. Recognizing and controlling for these lurking variables is essential for valid conclusions about the drug's effectiveness.

5. Aspects of the Study That Are Statistically Sound and Not

Statistically Sound Aspects:

• Use of randomization within gender blocks to assign patients reduces selection bias.
• Blinding of patients and nurses minimizes performance and detection biases.
• Measurement of recovery time as a quantitative and objective variable enhances measurement reliability.
• Comparing within_gender groups and across genders enhances understanding of differential effects.

Aspects That Are Not Statistically Sound or Could Be Improved:

• The small sample size of 30 patients limits the statistical power and generalizability of the findings.
• Potential confounders like age, severity of condition, or pre-existing health issues are not controlled or measured, which could bias the outcomes.
• External validity could be compromised if the patient population or hospital conditions differ significantly from broader populations.
• No mention of specific statistical tests used to determine significance, making it difficult to evaluate the robustness of the results.

Overall, while the study employs many sound principles such as randomization and blinding, limitations like small sample size and unmeasured confounders could impact the validity of the conclusions. For stronger results, larger samples and controlling for additional variables would be beneficial.

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