| Course | NUR-7630 Biostatistics and Clinical Epidemiology |
|---|---|
| Module | Module 2 |
| Paper type | Study designs, bias and confounding |
| Length | About 1,009 words, 6 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Doctor of Nursing Practice |
| Updated | October 2026 |
Free sample paper for NUR 7630 Module 2
Healthier Women, Not Healthier Hearts: Confounding, the Healthy User and Why a Trial Overturned Two Decades of Observational Evidence on Hormone Therapy
Student Name
Doctor of Nursing Practice, Thomas Edison State University
NUR-7630: Biostatistics and Clinical Epidemiology
Mentor Name
Month Day, Year
Healthier Women, Not Healthier Hearts: Confounding, the Healthy User and Why a Trial Overturned Two Decades of Observational Evidence on Hormone Therapy
The Question a Committee Asked
Our hospital's women's health committee is reviewing an education handout that still mentions heart protection as a benefit of hormone therapy, a claim common in patient materials of the 1990s. As a DNP student, I was asked to explain why that claim was dropped. The answer is a lesson in study design: observational studies and a randomized trial reached opposite conclusions, and understanding why tells us how to read every observational study that crosses a committee's table.
What the Observational Studies Showed
For two decades, cohort and case-control studies compared women who chose to take hormones after menopause with women who did not. Many found lower rates of coronary heart disease among users, and the finding was biologically plausible because estrogen improves cholesterol levels. Humphrey et al. (2002) pooled the good- and fair-quality observational studies. Taken together, those studies put the risk of dying of cardiovascular disease among hormone users at three quarters of nonusers' risk, an apparent benefit with a wide band of uncertainty. What happened next is the heart of the story. When they restricted the analysis to studies that adjusted for socioeconomic status as well as the usual risk factors, the summary relative risk for coronary disease incidence was 1.07, essentially no effect.
The Randomized Answer
In the Women's Health Initiative, upward of sixteen thousand women past menopause who still had a uterus were assigned by chance to combined hormones or a dummy pill. Roughly five years into follow-up the combined-hormone comparison was ended early: breast cancers had crossed the line the monitors had drawn in advance, and harms outweighed gains overall. Heart attacks and coronary deaths rose rather than fell on hormones, by about 29 percent, strokes by about 41 percent and lung clots roughly doubled, while hip fractures and colon cancers became less frequent (Writing Group for the Women's Health Initiative Investigators, 2002). The direction for the heart was the opposite of what the observational literature had led clinicians to expect.
| Evidence | Design | Coronary heart disease estimate |
|---|---|---|
| Observational studies, crude pooling | Cohort and case-control | Apparently protective for cardiovascular death |
| Observational studies adjusted for socioeconomic status | Cohort and case-control | Relative risk 1.07, no effect |
| Women's Health Initiative | Randomized trial | Hazard ratio 1.29, more events |
Confounding and the Healthy User
A confounder is a factor that is associated with the exposure and independently affects the outcome, and that is not on the causal path between them. The cohort studies compared women who picked hormones themselves with women who did not, and the two groups were not alike. As a group they had more money and schooling, kept regular appointments, exercised more and smoked less, and every one of those traits protects the heart without any help from estrogen. Lawlor et al. (2004) argued that confounding by socioeconomic position across the life course, which is hard to measure fully, could explain the observational findings, and that the episode should teach caution rather than abandonment of observational epidemiology.
The healthy user effect is a specific form of this confounding: people who start and stay on a preventive medication tend to be healthier in many unmeasured ways. Statistical adjustment can reduce confounding by measured variables, but it cannot remove confounding by variables that were measured poorly or not at all.
The same pattern has appeared elsewhere. Observational studies once suggested that antioxidant vitamins and other supplements prevented heart disease, and later trials found no benefit; in each case, the people who chose the preventive pill were people who already did many things to protect their health. Recognizing the pattern is more useful to a DNP than memorizing any single example, because the next promising observational finding will arrive with the same structure.
What Randomization Fixes
Randomization assigns treatment by chance, so on average the groups are balanced on all characteristics, measured and unmeasured, including income, education and health habits. Any difference in outcomes can then be attributed to the treatment or to chance, and chance can be quantified with a confidence interval. That is why the trial's finding outweighs the observational studies on a question of cause.
Randomized trials have limits of their own. The trial's volunteers were mostly in their sixties, a decade or more past menopause, whereas many cohort participants had started hormones near menopause; later work suggests timing may change the effect. A DNP reading both kinds of evidence should ask whether a disagreement reflects bias, a different population or both.
Trials also measure what they were designed to measure. The Women's Health Initiative answered whether starting combined hormones in older postmenopausal women prevents heart disease. It did not answer every question a woman near menopause might ask about her symptoms, and the committee's handout should not stretch it beyond that.
Lessons for Reading Observational Studies
Four questions follow from this case. Who chose the exposure, and how might they differ from those who did not? Which confounders were measured, and how well? Is there a plausible unmeasured confounder, such as socioeconomic status or health-seeking behavior? And does the effect shrink as more confounders are added, as it did here? An effect that weakens with each adjustment is a warning that what remains may also be confounding.
Recommendation to the Committee
The handout should be revised to remove any claim that hormone therapy protects the heart. It should describe hormone therapy as a treatment for menopausal symptoms, with risks and benefits that depend on age, time since menopause and individual risk factors, and should direct women to discuss these with their clinicians.
Conclusion
Observational studies suggested that hormone therapy protected women's hearts because the women who chose it were healthier in ways that adjustment could not fully capture. When studies accounted for socioeconomic status, the apparent benefit vanished, and a randomized trial found more coronary events. Confounding and the healthy user effect explain the reversal, and they remain the first questions a DNP should ask of any observational claim.
References
Humphrey, L. L., Chan, B. K. S., & Sox, H. C. (2002). Postmenopausal hormone replacement therapy and the primary prevention of cardiovascular disease. Annals of Internal Medicine, 137(4), 273-284. https://doi.org/10.7326/0003-4819-137-4-200208200-00012
Lawlor, D. A., Davey Smith, G., & Ebrahim, S. (2004). Commentary: The hormone replacement-coronary heart disease conundrum: Is this the death of observational epidemiology? International Journal of Epidemiology, 33(3), 464-467. https://doi.org/10.1093/ije/dyh124
Writing Group for the Women's Health Initiative Investigators. (2002). Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the Women's Health Initiative randomized controlled trial. JAMA, 288(3), 321-333. https://doi.org/10.1001/jama.288.3.321
What the NUR 7630 Module 2 instructions ask for
Study designs and data sources are named in the NUR-7630 catalog description, and with the course syllabus kept inside TESU's platform, this second module studies them through a case in which different designs disagreed. Expect to compare designs, define bias and confounding, identify them in specific studies and explain what randomization and adjustment can and cannot do. Some classrooms supply two articles on the same question and ask you to explain why they differ. Check whether you must use a named appraisal framework. Pick a question where the designs truly conflict, so that bias is something you must explain rather than define. Name the confounders you suspect before you read the authors' discussion.
How this NUR 7630 Module 2 example is built
The paper starts with a committee reviewing a handout that still claims heart protection from hormone therapy. A section on the observational studies reports a meta-analysis in which an apparent benefit disappeared after adjustment for socioeconomic status. The trial section reports the Women's Health Initiative's hazard ratios, and a three-row table sets the estimates side by side. Confounding and the healthy user effect are defined and applied, with a commentary on socioeconomic position. A section explains what randomization fixes and notes the trial's own population limits. Four questions for reading observational studies and a recommendation on the handout come before the conclusion. The point at which the apparent benefit disappeared is marked for the reader, since it is the key to the argument.
NUR 7630 Module 2 rubric: what earns full marks
Study design papers are graded on accurate definitions of bias and confounding, correct application to specific studies, an explanation of what randomization and adjustment achieve and balanced judgment about each design's limits. Mentors look for the mechanism of confounding spelled out, not just named. Showing how estimates changed with adjustment is strong evidence of understanding. Acknowledging the trial's own limits shows maturity, as does naming other examples where the same bias misled clinicians. Practical lessons for reading future studies demonstrate transfer. APA references, including group authors cited correctly, complete the grade. Readers also value a revised patient handout or other concrete product that shows the analysis changing practice.
NUR 7630 Module 2 help: mistakes that cost marks
Students often write that observational studies are biased and trials are not, without explaining how the bias arose in the studies in question. Name the confounders and show how they could produce the result. Another weakness is treating adjustment as a cure; explain that unmeasured or poorly measured confounders remain. Do not present the trial as flawless; consider its population. Cite group-authored reports with the group name in parentheses. Turn the analysis into questions you would ask of the next study, and recall other cases with the same shape. The desk will draft a design comparison for the two studies your NUR-7630 prompt assigns, free on the first order. Avoid presenting confounding as a single factor; several usually act together.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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NUR 7630 Module 2 questions, answered
Where can I find a free NUR 7630 Module 2 sample paper?
The hormone therapy design paper sits above, with its comparison table and notes on each sheet.
Why did observational studies suggest hormone therapy protected the heart?
Women who chose hormones were wealthier, more educated, more active and less likely to smoke. These confounders lowered their heart risk independently of the hormones.
What did the Women's Health Initiative find for coronary heart disease?
Estrogen plus progestin increased coronary events, with a hazard ratio of 1.29, along with increases in stroke and pulmonary embolism.
What is the healthy user effect?
People who begin a preventive drug and keep taking it tend to be healthier in ways a study never records, so the drug collects credit for their habits.
Can statistical adjustment remove confounding?
Only for confounders that were measured well. Unmeasured or poorly measured factors, such as socioeconomic position across life, can still bias the result.