Six results from a practice trial table on overnight earplugs and eye masks, relative risk to sleep-score gap, are interpreted in NU504's Unit 4 exercise for what each will and will not support. Searches like "nu 504 unit 4 assignment example", "nu504 unit 4 sample" and "nu504 unit 4 example" land here.
Six Numbers From One Trial Table: A Statistics Interpretation Exercise on Earplugs, Eye Masks and ICU Delirium
[Student Name]
Purdue University Global
NU504: Scientific and Analytic Approaches to Advanced Evidence-Based Practice
Unit 4 Assignment
[Instructor Name]
[Date]
The results interpreted here are a practice dataset of 114 patients per arm, modeled on the published ICU sleep trials; they are not a published study.
The Practice Result
Adults in a mixed ICU were randomized to earplugs and eye masks each night (n = 114) or usual care (n = 114). Delirium by CAM-ICU during the ICU stay: 25 of 114 (21.9%) with the aids, 38 of 114 (33.3%) without. Sleep quality on night three by the Richards-Campbell Sleep Questionnaire, scored 0 to 100 (Richards et al., 2000): mean 58.4 with the aids, 52.1 without, pooled standard deviation 19.7; scores were missing for 23 patients with the aids and 24 without, who could not self-report. The abstract concludes: "Earplugs and eye masks showed a trend toward reduced delirium."
Item 1: Relative Risk 0.66, 95% CI 0.43 to 1.01
What it is: the risk of delirium with the aids divided by the risk without them, 0.219 divided by 0.333. What it rests on: two proportions from 114 patients each. What it supports: the point estimate suggests about a one-third lower risk. The interval runs from 0.43, a reduction of more than half, to 1.01, essentially no difference. The data are compatible with a large benefit and with none, so the relative risk alone cannot tell a unit whether to act.
Item 2: Absolute Difference 11.4 Percentage Points, 95% CI -0.1 to 22.9
What it is: 33.3 percent minus 21.9 percent, about eleven fewer cases of delirium per hundred patients. What it rests on: the same counts, expressed on the scale a unit plans with. What it supports: a relative reduction of a third sounds larger than eleven per hundred, but a unit deciding how much night-shift time to spend fitting earplugs needs the absolute figure. The interval crosses zero by a tenth of a point, which matches the relative risk crossing one.
Item 3: Number Needed to Treat 9
What it is: 1 divided by 0.114, about 9 patients given the aids for one case of delirium prevented. What it rests on: the absolute difference, so it inherits that interval. What it supports: inverting the interval gives a number needed to treat of about 4 at the favorable end, and, because the interval crosses zero, it passes through no effect to a number needed to harm above 1,000 at the other end. A single number needed to treat without that caveat would overstate what the trial shows.
Item 4: Odds Ratio 0.56, 95% CI 0.31 to 1.01
What it is: the odds of delirium with the aids, 25 to 89, divided by the odds without them, 38 to 76. What it rests on: the same counts. What it supports: the odds ratio looks stronger than the relative risk, 0.56 against 0.66, because the outcome is common. When a third of control patients develop delirium, odds and risks diverge, and an odds ratio read as if it were a relative risk exaggerates the effect. The relative risk is the better summary for this table.
Item 5: Sleep-Score Gap 6.3 Points, 95% CI 0.6 to 12.0
What it is: the difference in mean questionnaire scores on night three, 58.4 against 52.1. What it rests on: only the 91 and 90 patients who could rate their own sleep; a fifth of each arm is missing, and those missing are likely the sickest or already delirious. What it supports: the difference is statistically significant, p = 0.03, but its standardized size is about 0.32 standard deviations, a small effect. Statistical significance here says the gap is unlikely to be zero among patients able to answer, not that it matters clinically, and not that it applies to patients who could not answer.
Item 6: p = 0.054, and the Abstract's Conclusion
What it is: the probability of a difference at least this large if the aids had no effect at all. What it rests on: a single test at the conventional 0.05 threshold. What it supports: very little on its own. A p value of 0.054 is not evidence of no effect; it is weak evidence, and it sits beside an interval that includes both a large benefit and none (Altman & Bland, 1995). Treating 0.054 as a failure and 0.049 as a success would be treating an arbitrary line as a property of the data.
The abstract's word "trend" implies the result was moving toward significance, which is not something a single analysis can do. A rewritten conclusion keeps every number and drops the word: "Delirium occurred in 21.9 percent of patients given earplugs and eye masks and 33.3 percent of controls (relative risk 0.66, 95% CI 0.43 to 1.01). The trial could not exclude no effect, and a benefit as large as a halving of risk remains possible; a larger trial with blinded outcome assessment is needed."
What the Six Items Add Up To
Read together, the six numbers tell a consistent story: a plausible, clinically meaningful reduction in delirium that this trial was too small to establish, and a small improvement in sleep among the patients well enough to report it. For a practice decision, the absolute difference and its interval are the most useful pair, and the odds ratio and the p value are the least. Polit and Beck (2021) make the same point in general terms: an estimate with its interval says how large an effect could be, while a p value says only whether chance is a plausible explanation.
References
Altman, D. G., & Bland, J. M. (1995). Absence of evidence is not evidence of absence. BMJ, 311(7003), 485. https://doi.org/10.1136/bmj.311.7003.485
Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.
Richards, K. C., O'Sullivan, P. S., & Phillips, R. L. (2000). Measurement of sleep in critically ill patients. Journal of Nursing Measurement, 8(2), 131-144. https://doi.org/10.1891/1061-3749.8.2.131
How this NU504 Unit 4 example is structured
The exercise works from a clearly labeled practice dataset of 114 patients per arm, and each of its six items makes the same three moves: what the number is, what it rests on, and what it will and will not support. The relative risk and the absolute difference are paired, since a unit deciding on staff time needs eleven fewer cases per hundred more than a relative reduction of a third. The number needed to treat is reported with its interval inverted, through no effect to harm. The odds ratio is shown drifting from the relative risk because the outcome is common, and the sleep item separates significance from importance, with a standardized difference of 0.32 and a fifth of each arm missing. The last item rewrites the abstract's conclusion without the word trend.
Get an NU504 Unit 4 example written to your instructions
Paste the results section or reported figures from your Unit 4 exercise, plus its questions and the rubric. Each number comes back interpreted in the same three moves, calculations shown and intervals kept attached, so every step of the reasoning is open to checking. The first custom sample carries no charge and is back in 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
NU504 Unit 4 questions, answered
How do relative risk and absolute risk reduction differ?
Relative risk compares the proportion of patients with the outcome in one group to the proportion in the other, so 0.66 means about a third fewer events. Absolute risk reduction subtracts one proportion from the other, here 33.3 minus 22.0 percent. The relative figure tends to travel across settings with different baseline risk; the absolute one tells a unit how many patients would actually benefit.
How is a number needed to treat calculated?
Divide one by the absolute risk reduction expressed as a proportion, then round up. An 11.3-point difference is 0.113, and one divided by 0.113 is about 8.9, reported as 9. The interval comes from inverting the bounds of the absolute difference; when that interval crosses zero, the NNT interval runs from benefit through infinity to harm, and should be reported that way.
Is a result with p just above 0.05 worthless?
No. A p value near 0.05 says the data are only moderately incompatible with no effect; it does not show the effect is zero. The confidence interval is more informative, because it shows which effect sizes remain plausible. An interval running from a large benefit to almost none calls for a bigger trial, not for dismissing the intervention or declaring it effective.