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Since 1976 the Nurses' Health Study has followed more than 100,000 nurses. Every two years, the nurses fill out a questionnaire about their habits and their health. Results from this study indicated that postmenopausal women have a reduced risk of heart disease if they take a hormone replacement drug. a. Suppose the hormone-replacement drug actually has no effect. Identify a potential lurking variable that could explain the results of the observational study. (Hint: Suppose that the women who took the drug tended to be more conscientious about their personal health than those who did not take it.) b. Recently a randomized experiment called the Women's Health Initiative was conducted by the National Institutes of Health to see whether hormone therapy is truly helpful. The study, planned to last for eight years, was stopped after five years when analyses showed that women who took hormones had \(30 \%\) more heart attacks. This study suggested that rather than reducing the risk of heart attacks, hormone replacement drugs actually increase the risk. \({ }^{3}\) How is it that two studies could reach such different conclusions? (For attempts to reconcile the studies, see a story by Gina Kolata in The New York Times, April 21, 2003.) c. Explain why randomized experiments, when feasible, are preferable to observational studies.

Step by step solution

01

Understand the Study Context

To solve this question, understand that the Nurses' Health Study is an observational study based on self-reported data from nurses about their lifestyle and health habits, particularly focused on postmenopausal hormone replacement therapy. The study suggests a reduction in heart disease risk among users of the therapy.

02

Identify Lurking Variables (Part a)

A potential lurking variable could be the overall health consciousness of the women taking hormone-replacement drugs. These women may engage in other health-positive behaviors such as regular exercise, balanced diet, and regular health check-ups, which could contribute to a reduced risk of heart disease, confounding the study results.

03

Compare Study Results (Part b)

Two studies—the Nurses' Health Study (observational) and the Women's Health Initiative (randomized experiment)—may have different conclusions due to methodology. The observational study's results could be skewed by confounding variables, while the randomized experiment minimizes these effects and provides more reliable conclusions. In this case, the randomized study indicated increased heart attack risks, contrary to the observational study.

04

Explain the Superiority of Randomized Experiments (Part c)

Randomized experiments are preferable because they assign participants to treatment or control groups randomly, eliminating the influence of lurking variables and providing a clearer causal relationship between the treatment and its effects. This leads to more reliable and valid conclusions compared to observational studies.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Randomized Experiment

A randomized experiment is a powerful method used to determine cause-and-effect relationships. Here, participants are randomly assigned to either a treatment group or a control group. By random assignment, this method ensures that each participant has an equal chance of receiving the treatment, which helps eliminate biases.
This approach is considered the gold standard in research for a few important reasons:

• It controls for both known and unknown variables, which might affect the outcome.
• By isolating the treatment's effect, any differences in results between groups can be attributed more confidently to the treatment itself.
• The randomization process helps to distribute lurking variables evenly across the groups.

This means that randomized experiments, unlike observational studies, offer clearer insight into whether a treatment actually causes an effect, making the results more reliable for decision-making in healthcare and policy. The Women's Health Initiative is an example of such a study, showing the risks associated with hormone replacement therapy more clearly than observational methods could.

Lurking Variable

A lurking variable is an unobserved variable that influences the outcome of a study by affecting both the independent and dependent variables. This kind of variable can introduce bias or confounding variables, which are distorted associations between studied factors.
In the context of the Nurses' Health Study, an observational study, a key lurking variable could be the overall health consciousness of women taking hormone replacements. These individuals might also engage in other heart-healthy practices, such as:

• Maintaining a balanced diet.
• Exercising regularly.
• Attending routine health check-ups.

This health consciousness could result in lower heart disease incidence, independent of the hormone therapy itself. As a result, observational studies might attribute the reduced heart disease risk to hormone replacement therapy inaccurately. Ignoring lurking variables can lead to incorrect conclusions, which is why understanding their influence is crucial in research.

Causal Relationship

Establishing a causal relationship means showing that one event is the cause of another event. In scientific research, it is important to demonstrate that changes in the independent variable directly result in changes in the dependent variable.
Randomized experiments are particularly useful for this because they remove confounding variables through random assignment. Here's how such experiments can establish causal relationships clearly:

• Randomization helps ensure that any differences seen between groups are due to the treatment and not other factors.
• Controlling the environment prevents external variables from influencing the outcome.
• Clear definitions of independent and dependent variables allow researchers to see direct connections between them.

In the case of hormone therapy research, the Women's Health Initiative's randomized approach revealed a clearer, and contrary, view to the observational studies. They discovered increased risks for heart attacks, establishing a stronger and more direct causal link between hormone therapy and heart health.